Methods, systems, and devices for handling inserted data into captured images

ABSTRACT

Computationally implemented methods and systems include acquiring a captured image, wherein the captured image depicts at least one entity, performing a comparison between an estimated potential revenue generated from a distribution of the captured image and an estimated potential liability incurred for the distribution of the captured image, and modifying the captured image by placing at least one advertisement image at a particular location of the captured image. In addition to the foregoing, other aspects are described in the claims, drawings, and text.

CROSS-REFERENCE TO RELATED APPLICATIONS

If an Application Data Sheet (ADS) has been filed on the filing date ofthis application, it is incorporated by reference herein. Anyapplications claimed on the ADS for priority under 35 U.S.C. §§119, 120,121, or 365(c), and any and all parent, grandparent, great-grandparent,etc. applications of such applications, are also incorporated byreference, including any priority claims made in those applications andany material incorporated by reference, to the extent such subjectmatter is not inconsistent herewith.

The present application is related to and/or claims the benefit of theearliest available effective filing date(s) from the following listedapplication(s) (the “Priority Applications”), if any, listed below(e.g., claims earliest available priority dates for other thanprovisional patent applications or claims benefits under 35 USC §119(e)for provisional patent applications, for any and all parent,grandparent, great-grandparent, etc. applications of the PriorityApplication(s)). In addition, the present application is related to the“Related Applications,” if any, listed below.

PRIORITY APPLICATIONS

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 14/051,213, entitled METHODS, SYSTEMS, AND DEVICESFOR FACILITATING VIABLE DISTRIBUTION OF DATA COLLECTED BY WEARABLECOMPUTATION, naming Pablos Holman, Roderick A. Hyde, Royce A. Levien,Richard T. Lord, Robert W. Lord, and Mark A. Malamud as inventors, filed10 Oct. 2013 with attorney docket no. 0213-003-060-000000, which iscurrently co-pending or is an application of which a currentlyco-pending application is entitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 14/055,471, entitled METHODS, SYSTEMS, AND DEVICESFOR HANDLING IMAGE DATA FROM CAPTURED IMAGES, naming Pablos Holman,Roderick A. Hyde, Royce A. Levien, Richard T. Lord, Robert W. Lord, andMark A. Malamud as inventors, filed 16 Oct. 2013 with attorney docketno. 0213-003-061-000000, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 14/055,543, entitled METHODS, SYSTEMS, AND DEVICESFOR HANDLING IMAGE DATA FROM CAPTURED IMAGES, naming Pablos Holman,Roderick A. Hyde, Royce A. Levien, Richard T. Lord, Robert W. Lord, andMark A. Malamud as inventors, filed 16 Oct. 2013 with attorney docketno. 0213-003-072-000000, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 14/084,254, entitled DEVICES, METHODS, AND SYSTEMSFOR ANALYZING CAPTURED IMAGE DATA AND PRIVACY DATA, naming PablosHolman, Roderick A. Hyde, Royce A. Levien, Richard T. Lord, Robert W.Lord, and Mark A. Malamud as inventors, filed 19 Nov. 2013 with attorneydocket no. 0213-003-062-000000, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 14/084,579 entitled DEVICES, METHODS, AND SYSTEMSFOR ANALYZING CAPTURED IMAGE DATA AND PRIVACY DATA, naming PablosHolman, Roderick A. Hyde, Royce A. Levien, Richard T. Lord, Robert W.Lord, and Mark A. Malamud as inventors, filed 19 Nov. 2013 with attorneydocket no. 0213-003-073-000000, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 14/084,581, entitled METHODS, SYSTEMS, AND DEVICESFOR HANDLING IMAGE DATA FROM CAPTURED IMAGES, naming Pablos Holman,Roderick A. Hyde, Royce A. Levien, Richard T. Lord, Robert W. Lord, andMark A. Malamud as inventors, filed 19 Nov. 2013 with attorney docketno. 0213-003-063-000000, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 14/084,591, entitled METHODS, SYSTEMS, AND DEVICESFOR HANDLING IMAGE DATA FROM CAPTURED IMAGES, naming Pablos Holman,Roderick A. Hyde, Royce A. Levien, Richard T. Lord, Robert W. Lord, andMark A. Malamud as inventors, filed 19 Nov. 2013 with attorney docketno. 0213-003-074-000000, which is currently co-pending or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

RELATED APPLICATIONS

U.S. patent application Ser. No. 14/108,077, entitled METHODS, SYSTEMS,AND DEVICES FOR DELIVERING IMAGE DATA FROM CAPTURED IMAGES TO DEVICES,naming Pablos Holman, Roderick A. Hyde, Royce A. Levien, Richard T.Lord, Robert W. Lord, and Mark A. Malamud as inventors, filed 16 Dec.2013 with attorney docket no. 0213-003-064-000000, is related to thepresent application.

U.S. patent application Ser. No. 14/108,107, entitled METHODS, SYSTEMS,AND DEVICES FOR DELIVERING IMAGE DATA FROM CAPTURED IMAGES TO DEVICES,naming Pablos Holman, Roderick A. Hyde, Royce A. Levien, Richard T.Lord, Robert W. Lord, and Mark A. Malamud as inventors, filed 16 Dec.2013 with attorney docket no. 0213-003-075-000000, is related to thepresent application.

The United States Patent Office (USPTO) has published a notice to theeffect that the USPTO's computer programs require that patent applicantsreference both a serial number and indicate whether an application is acontinuation, continuation-in-part, or divisional of a parentapplication. Stephen G. Kunin, Benefit of Prior-Filed Application, USPTOOfficial Gazette Mar. 18, 2003. The USPTO further has provided forms forthe Application Data Sheet which allow automatic loading ofbibliographic data but which require identification of each applicationas a continuation, continuation-in-part, or divisional of a parentapplication. The present Applicant Entity (hereinafter “Applicant”) hasprovided above a specific reference to the application(s) from whichpriority is being claimed as recited by statute. Applicant understandsthat the statute is unambiguous in its specific reference language anddoes not require either a serial number or any characterization, such as“continuation” or “continuation-in-part,” for claiming priority to U.S.patent applications. Notwithstanding the foregoing, Applicantunderstands that the USPTO's computer programs have certain data entryrequirements, and hence Applicant has provided designation(s) of arelationship between the present application and its parentapplication(s) as set forth above and in any ADS filed in thisapplication, but expressly points out that such designation(s) are notto be construed in any way as any type of commentary and/or admission asto whether or not the present application contains any new matter inaddition to the matter of its parent application(s).

If the listings of applications provided above are inconsistent with thelistings provided via an ADS, it is the intent of the Applicant to claimpriority to each application that appears in the Priority Applicationssection of the ADS and to each application that appears in the PriorityApplications section of this application.

All subject matter of the Priority Applications and the RelatedApplications and of any and all parent, grandparent, great-grandparent,etc. applications of the Priority Applications and the RelatedApplications, including any priority claims, is incorporated herein byreference to the extent such subject matter is not inconsistentherewith.

BACKGROUND

This application is related to the capture of images that may includepersonality rights.

SUMMARY

Recently, there has been an increased popularity in wearable computers,e.g., computers that are placed in articles of clothing or clothingaccessories, e.g., watches, eyeglasses, shoes, jewelry, accessories,shirts, pants, headbands, and the like. As technology allows electronicdevices to become smaller and smaller, more and more items may be“smart” items, e.g., may contain a computer.

In addition, image capturing technology has also improved, allowing forhigh quality digital cameras that can capture pictures, audio, video, ora combination thereof. These digital cameras may be small enough to fitonto wearable computers, e.g., inside of eyeglasses. In some instances,the digital camera may blend into the eyeglasses mold, and may not beimmediately recognizable as a camera. Such eyeglasses may beindistinguishable or somewhat distinguishable from standard eyeglassesthat do not contain a camera and/or a computer.

Further, the cost of data storage has decreased dramatically, and it isnot uncommon for an average person in a developed nation to have accessto enough digital storage to store months' and/or years' worth of videoand pictures. As the cost of data storage has decreased dramatically, sotoo has the cost of processors to process that data, meaning thatautomation may be able to take an entire day's worth of surreptitiousrecording, and isolate those portions of the recording that capturedpersons, either specific persons or persons in general.

Accordingly, with technology, it is possible for a person to “wear” acomputer, in the form of eyeglasses, watches, shirts, hats, or through apocket-sized device carried by a person, e.g., a cellular telephonedevice. This wearable computer may be used to record people, e.g., tocapture pictures, audio, video, or a combination thereof a person,without their knowledge. Thus, conversations that a person may assume tobe private, may be recorded and widely distributed. Moreover, a personmay be surreptitiously recorded while they are in a locker room, in abathroom, or in a telephone booth. It may be difficult or impossible totell when a person is being recorded. Further, once proliferation ofthese wearable computers with digital cameras becomes widespread, peoplemust assume that they are under surveillance 100% of the time that theyare not in their house.

Therefore, a need has arisen to provide systems that attempt to limitthe capture and distribution of a person's personality rights. Thepresent invention is directed to devices, methods, and systems thatattempt to limit the capture and distribution of captured images ofpersons. Specifically, the present invention is directed to devices,methods, and systems that attempt to limit the capture and distributionof captured images of persons, implemented at a device that carries outthe capturing of the image. In some embodiments, this device may be awearable computer, but in other embodiments, any image capturing deviceor any device that has an image capturing device incorporated into itsfunctionality may implement the devices, methods, and systems describedherein.

The instant application is directed to devices, methods, and systemsthat have a capability to capture images, and in which the capture ofthose images may include capturing images of a person, persons, orportion(s) of a person for which a privacy beacon may be associated. Theprivacy beacon may be optical, digital, or other form (e.g., radio,electromagnetic, biomechanic, quantum-state, and the like), and may bedetected through digital or optical operations, as discussed herein. Theinstant application describes devices, methods and systems that mayinterface with other parts of a larger system, which may be described indetail in this or other applications.

In one or more various aspects, a method includes but is not limited toacquiring a captured image, wherein the captured image depicts at leastone entity, performing a comparison between an estimated potentialrevenue generated from a distribution of the captured image and anestimated potential liability incurred for the distribution of thecaptured image, modifying the captured image by placing at least oneadvertisement image at a particular location of the captured image, andcalculating an updated estimated potential revenue from the modifiedcaptured image that includes the at least one advertisement image at theparticular location. In addition to the foregoing, other method aspectsare described in the claims, drawings, and text forming a part of thedisclosure set forth herein.

In one or more various aspects, one or more related systems may beimplemented in machines, compositions of matter, or manufactures ofsystems, limited to patentable subject matter under 35 U.S.C. 101. Theone or more related systems may include, but are not limited to,circuitry and/or programming for carrying out the herein-referencedmethod aspects. The circuitry and/or programming may be virtually anycombination of hardware, software, and/or firmware configured to effectthe herein-referenced method aspects depending upon the design choicesof the system designer, and limited to patentable subject matter under35 USC 101.

In one or more various aspects, a system includes, but is not limitedto, means for acquiring a captured image, wherein the captured imagedepicts at least one entity, means for performing a comparison betweenan estimated potential revenue generated from a distribution of thecaptured image and an estimated potential liability incurred for thedistribution of the captured image, means for modifying the capturedimage by placing at least one advertisement image at a particularlocation of the captured image, and means for calculating an updatedestimated potential revenue from the modified captured image thatincludes the at least one advertisement image at the particularlocation. In addition to the foregoing, other system aspects aredescribed in the claims, drawings, and text forming a part of thedisclosure set forth herein.

In one or more various aspects, a system includes, but is not limitedto, circuitry for acquiring a captured image, wherein the captured imagedepicts at least one entity, circuitry for performing a comparisonbetween an estimated potential revenue generated from a distribution ofthe captured image and an estimated potential liability incurred for thedistribution of the captured image, circuitry for modifying the capturedimage by placing at least one advertisement image at a particularlocation of the captured image, and calculating an updated estimatedpotential revenue from the modified captured image that includes the atleast one advertisement image at the particular location. In addition tothe foregoing, other system aspects are described in the claims,drawings, and text forming a part of the disclosure set forth herein.

In one or more various aspects, a computer program product, comprising asignal bearing medium, bearing one or more instructions including, butnot limited to, one or more instructions for acquiring a captured image,wherein the captured image depicts at least one entity, one or moreinstructions for performing a comparison between an estimated potentialrevenue generated from a distribution of the captured image and anestimated potential liability incurred for the distribution of thecaptured image, one or more instructions for modifying the capturedimage by placing at least one advertisement image at a particularlocation of the captured image, and one or more instructions forcalculating an updated estimated potential revenue from the modifiedcaptured image that includes the at least one advertisement image at theparticular location. In addition to the foregoing, other computerprogram product aspects are described in the claims, drawings, and textforming a part of the disclosure set forth herein.

In one or more various aspects, a device is defined by a computationallanguage, such that the device comprises one or more interchainedphysical machines ordered for acquiring a captured image, wherein thecaptured image depicts at least one entity, one or more interchainedphysical machines ordered for performing a comparison between anestimated potential revenue generated from a distribution of thecaptured image and an estimated potential liability incurred for thedistribution of the captured image, one or more interchained physicalmachines ordered for modifying the captured image by placing at leastone advertisement image at a particular location of the captured image,and one or more interchained physical machines ordered for calculatingan updated estimated potential revenue from the modified captured imagethat includes the at least one advertisement image at the particularlocation.

In addition to the foregoing, various other method and/or system and/orprogram product aspects are set forth and described in the teachingssuch as text (e.g., claims and/or detailed description) and/or drawingsof the present disclosure.

The foregoing is a summary and thus may contain simplifications,generalizations, inclusions, and/or omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is NOT intended to be in any way limiting. Otheraspects, features, and advantages of the devices and/or processes and/orother subject matter described herein will become apparent by referenceto the detailed description, the corresponding drawings, and/or in theteachings set forth herein.

BRIEF DESCRIPTION OF THE FIGURES

For a more complete understanding of embodiments, reference now is madeto the following descriptions taken in connection with the accompanyingdrawings. The use of the same symbols in different drawings typicallyindicates similar or identical items, unless context dictates otherwise.The illustrative embodiments described in the detailed description,drawings, and claims are not meant to be limiting. Other embodiments maybe utilized, and other changes may be made, without departing from thespirit or scope of the subject matter presented here.

FIG. 1, including FIGS. 1-A through 1-T, shows a high-level systemdiagram of one or more exemplary environments in which transactions andpotential transactions may be carried out, according to one or moreembodiments. FIG. 1 forms a partially schematic diagram of anenvironment(s) and/or an implementation(s) of technologies describedherein when FIGS. 1-A through 1-T are stitched together in the mannershown in FIG. 1-P, which is reproduced below in table format.

In accordance with 37 C.F.R. §1.84(h)(2), FIG. 1 shows “a view of alarge machine or device in its entirety . . . broken into partial views. . . extended over several sheets” labeled FIG. 1-A through FIG. 1-T(Sheets 1-20). The “views on two or more sheets form, in effect, asingle complete view, [and] the views on the several sheets . . . [are]so arranged that the complete figure can be assembled” from “partialviews drawn on separate sheets . . . linked edge to edge. Thus, in FIG.1, the partial view FIGS. 1-A through 1-T are ordered alphabetically, byincreasing in columns from left to right, and increasing in rows top tobottom, as shown in the following table:

(1,1) - FIG. (1,2) - FIG. (1,3) - FIG. (1,4) - FIG. (1,5) - FIG. 1-A 1-B1-C 1-D 1-E (2,1) - FIG. (2,2) - FIG. (2,3) - FIG. (2,4) - FIG. (2,5) -FIG. 1-F 1-G 1-H 1-I 1-J (3,1) - FIG. (3,2) - FIG. (3,3) - FIG. (3,4) -FIG. (3,5) - FIG. 1-K 1-L 1-M 1-N 1-O (4,1) - FIG. (4,2) - FIG. (4,3) -FIG. (4,4) - FIG. (4,5) - FIG. 1-P 1-Q 1-R 1-S 1-TTable 1. Table showing alignment of enclosed drawings to form partialschematic of one or more environments.

In accordance with 37 C.F.R. §1.84(h)(2), FIG. 1 is “ . . . a view of alarge machine or device in its entirety . . . broken into partial views. . . extended over several sheets . . . [with] no loss in facility ofunderstanding the view.” The partial views drawn on the several sheetsindicated in the above table are capable of being linked edge to edge,so that no partial view contains parts of another partial view. As here,“where views on two or more sheets form, in effect, a single completeview, the views on the several sheets are so arranged that the completefigure can be assembled without concealing any part of any of the viewsappearing on the various sheets.” 37 C.F.R. §1.84(h)(2).

It is noted that one or more of the partial views of the drawings may beblank, or may not contain substantive elements (e.g., may show onlylines, connectors, and the like). These drawings are included in orderto assist readers of the application in assembling the single completeview from the partial sheet format required for submission by the USPTO,and, while their inclusion is not required and may be omitted in this orother applications, their inclusion is proper, and should be consideredintentional.

FIG. 1-A, when placed at position (1,1), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-B, when placed at position (1,2), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-C, when placed at position (1,3), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-D, when placed at position (1,4), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-E, when placed at position (1,5), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-F, when placed at position (2,1), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-G, when placed at position (2,2), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-H, when placed at position (2,3), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-I, when placed at position (2,4), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-J, when placed at position (2,5), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-K, when placed at position (3,1), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-L, when placed at position (3,2), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-M, when placed at position (3,3), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-N, when placed at position (3,4), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-O, when placed at position (3,5), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-P, when placed at position (4,1), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-Q, when placed at position (4,2), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-R, when placed at position (4,3), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-S, when placed at position (4,4), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 1-T, when placed at position (4,5), forms at least a portion of apartially schematic diagram of an environment(s) and/or animplementation(s) of technologies described herein.

FIG. 2A shows a high-level block diagram of an exemplary environment200, according to one or more embodiments.

FIG. 2B shows a high-level block diagram of a server device, e.g., anadvertisement server device 237 operating in an exemplary environment200, according to one or more embodiments.

FIG. 3A shows a high-level block diagram of an exemplary image capturingdevice 302, according to one or more embodiments.

FIG. 3B shows a high-level block diagram of an exemplary image capturingdevice 304, according to one or more embodiments.

FIG. 3C shows a high-level block diagram of an exemplary image capturingdevice 306, according to one or more embodiments.

FIG. 3D shows a high-level block diagram of an exemplary image capturingdevice 308, according to one or more embodiments.

FIG. 3E shows a high-level block diagram of an exemplary image capturingdevice 309, according to one or more embodiments.

FIG. 4A shows a high-level block diagram of an exemplary environment400A including a computing device 420A and a server device 430A.

FIG. 4B shows a high-level block diagram of an exemplary environment400B including a computing device 420B and a server device 430B.

FIG. 4C shows a high-level block diagram of an exemplary environment400C including a computing device 420C and a server device 430C.

FIG. 4D shows a high-level block diagram of an exemplary environment400D including a computing device 420D and a server device 430D.

FIG. 4E shows a high-level block diagram of an exemplary environment400E including a computing device 420E and a server device 430E.

FIG. 5A shows a high-level block diagram of an exemplary environment500A including a computing device 520A and a server device 530A.

FIG. 5B shows a high-level block diagram of an exemplary environment500B including a computing device 520B and a server device 530B.

FIG. 5C shows a high-level block diagram of an exemplary environment500C including a computing device 520C and a server device 530C.

FIG. 5D shows a high-level block diagram of an exemplary environment500D including a computing device 520D and a server device 530D.

FIG. 6A shows an exemplary captured image 24 that features the entity105 and a modified image 14A that includes the advertisement 106,according to one or more embodiments.

FIG. 6B shows an exemplary captured image 24 that features the entity105 and a modified image 14B that includes the advertisement 106,according to one or more embodiments.

FIG. 6C shows an exemplary captured image 24A that features the entity105 and the advertisement 106, and a modified image 14C that includesthe advertisement 106, according to one or more embodiments.

FIG. 7, including FIGS. 7A-7C, shows a particular perspective of acaptured image that depicts at least one entity receiving module 252 ofprocessing module 250 of advertisement server device 237 of FIG. 2B,according to an embodiment.

FIG. 8, including FIGS. 8A-8G, shows a particular perspective of anestimated potential revenue generated from captured image distributionand estimated potential liability created from captured imagedistribution comparing module 254 of processing module 250 ofadvertisement server device 237 of FIG. 2B, according to an embodiment.

FIG. 9, including FIGS. 9A-9D, shows a particular perspective of an atleast one advertisement image inserting into the captured image at aparticular location module 256 of processing module 250 of advertisementserver device 237 of FIG. 2B, according to an embodiment.

FIG. 10, including FIGS. 10A-10B, shows a particular perspective of aupdated estimated potential revenue from the captured image with theinserted at least one advertisement image at the particular locationcalculating module 258 of processing module 250 of advertisement serverdevice 237 of FIG. 2B, according to an embodiment.

FIG. 11 is a high-level logic flowchart of a process, e.g., operationalflow 1100, according to an embodiment.

FIG. 12A is a high-level logic flow chart of a process depictingalternate implementations of an acquiring a captured image operation1102, according to one or more embodiments.

FIG. 12B is a high-level logic flow chart of a process depictingalternate implementations of an acquiring a captured image operation1102, according to one or more embodiments.

FIG. 12C is a high-level logic flow chart of a process depictingalternate implementations of an acquiring a captured image operation1102, according to one or more embodiments.

FIG. 13A is a high-level logic flow chart of a process depictingalternate implementations of a performing a comparison operation 1104,according to one or more embodiments.

FIG. 13B is a high-level logic flow chart of a process depictingalternate implementations of a performing a comparison operation 1104,according to one or more embodiments.

FIG. 13C is a high-level logic flow chart of a process depictingalternate implementations of a performing a comparison operation 1104,according to one or more embodiments.

FIG. 13D is a high-level logic flow chart of a process depictingalternate implementations of a performing a comparison operation 1104,according to one or more embodiments.

FIG. 13E is a high-level logic flow chart of a process depictingalternate implementations of a performing a comparison operation 1104,according to one or more embodiments.

FIG. 13F is a high-level logic flow chart of a process depictingalternate implementations of a performing a comparison operation 1104,according to one or more embodiments.

FIG. 13G is a high-level logic flow chart of a process depictingalternate implementations of a performing a comparison operation 1104,according to one or more embodiments.

FIG. 14A is a high-level logic flow chart of a process depictingalternate implementations of a modifying the captured image operation1106, according to one or more embodiments.

FIG. 14B is a high-level logic flow chart of a process depictingalternate implementations of a modifying the captured image operation1106, according to one or more embodiments.

FIG. 14C is a high-level logic flow chart of a process depictingalternate implementations of a modifying the captured image operation1106, according to one or more embodiments.

FIG. 14D is a high-level logic flow chart of a process depictingalternate implementations of a modifying the captured image operation1106, according to one or more embodiments.

FIG. 15A is a high-level logic flow chart of a process depictingalternate implementations of a calculating an updated estimatedpotential revenue operation 1108, according to one or more embodiments.

FIG. 15B is a high-level logic flow chart of a process depictingalternate implementations of a calculating an updated estimatedpotential revenue operation 1108, according to one or more embodiments.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar or identical components oritems, unless context dictates otherwise. The illustrative embodimentsdescribed in the detailed description, drawings, and claims are notmeant to be limiting. Other embodiments may be utilized, and otherchanges may be made, without departing from the spirit or scope of thesubject matter presented here.

Thus, in accordance with various embodiments, computationallyimplemented methods, systems, circuitry, articles of manufacture,ordered chains of matter, and computer program products are designed to,among other things, provide an interface for acquiring a captured image,wherein the captured image depicts at least one entity, performing acomparison between an estimated potential revenue generated from adistribution of the captured image and an estimated potential liabilityincurred for the distribution of the captured image, modifying thecaptured image by placing at least one advertisement image at aparticular location of the captured image, and calculating an updatedestimated potential revenue from the modified captured image thatincludes the at least one advertisement image at the particularlocation.

The claims, description, and drawings of this application may describeone or more of the instant technologies in operational/functionallanguage, for example as a set of operations to be performed by acomputer. Such operational/functional description in most instanceswould be understood by one skilled the art as specifically-configuredhardware (e.g., because a general purpose computer in effect becomes aspecial purpose computer once it is programmed to perform particularfunctions pursuant to instructions from program software (e.g., ahigh-level computer program serving as a hardware specification)).

Importantly, although the operational/functional descriptions describedherein are understandable by the human mind, they are not abstract ideasof the operations/functions divorced from computational implementationof those operations/functions. Rather, the operations/functionsrepresent a specification for massively complex computational machinesor other means. As discussed in detail below, the operational/functionallanguage must be read in its proper technological context, i.e., asconcrete specifications for physical implementations.

The logical operations/functions described herein are a distillation ofmachine specifications or other physical mechanisms specified by theoperations/functions such that the otherwise inscrutable machinespecifications may be comprehensible to a human reader. The distillationalso allows one of skill in the art to adapt the operational/functionaldescription of the technology across many different specific vendors'hardware configurations or platforms, without being limited to specificvendors' hardware configurations or platforms.

Some of the present technical description (e.g., detailed description,drawings, claims, etc.) may be set forth in terms of logicaloperations/functions. As described in more detail herein, these logicaloperations/functions are not representations of abstract ideas, butrather are representative of static or sequenced specifications ofvarious hardware elements. Differently stated, unless context dictatesotherwise, the logical operations/functions will be understood by thoseof skill in the art to be representative of static or sequencedspecifications of various hardware elements. This is true because toolsavailable to one of skill in the art to implement technical disclosuresset forth in operational/functional formats—tools in the form of ahigh-level programming language (e.g., C, java, visual basic), etc.), ortools in the form of Very high speed Hardware Description Language(“VHDL,” which is a language that uses text to describe logiccircuits)—are generators of static or sequenced specifications ofvarious hardware configurations. This fact is sometimes obscured by thebroad term “software,” but, as shown by the following explanation, thoseskilled in the art understand that what is termed “software” is ashorthand for a massively complex interchaining/specification ofordered-matter elements. The term “ordered-matter elements” may refer tophysical components of computation, such as assemblies of electroniclogic gates, molecular computing logic constituents, quantum computingmechanisms, etc.

For example, a high-level programming language is a programming languagewith strong abstraction, e.g., multiple levels of abstraction, from thedetails of the sequential organizations, states, inputs, outputs, etc.,of the machines that a high-level programming language actuallyspecifies. See, e.g., Wikipedia, High-level programming language,http://en.wikipedia.org/wiki/High-level_programming_language (as of Jun.5, 2012, 21:00 GMT). In order to facilitate human comprehension, in manyinstances, high-level programming languages resemble or even sharesymbols with natural languages. See, e.g., Wikipedia, Natural language,http://en.wikipedia.org/wiki/Natural_language (as of Jun. 5, 2012, 21:00GMT).

It has been argued that because high-level programming languages usestrong abstraction (e.g., that they may resemble or share symbols withnatural languages), they are therefore a “purely mental construct”(e.g., that “software”—a computer program or computer programming—issomehow an ineffable mental construct, because at a high level ofabstraction, it can be conceived and understood by a human reader). Thisargument has been used to characterize technical description in the formof functions/operations as somehow “abstract ideas.” In fact, intechnological arts (e.g., the information and communicationtechnologies) this is not true.

The fact that high-level programming languages use strong abstraction tofacilitate human understanding should not be taken as an indication thatwhat is expressed is an abstract idea. In fact, those skilled in the artunderstand that just the opposite is true. If a high-level programminglanguage is the tool used to implement a technical disclosure in theform of functions/operations, those skilled in the art will recognizethat, far from being abstract, imprecise, “fuzzy,” or “mental” in anysignificant semantic sense, such a tool is instead a nearincomprehensibly precise sequential specification of specificcomputational machines—the parts of which are built up byactivating/selecting such parts from typically more generalcomputational machines over time (e.g., clocked time). This fact issometimes obscured by the superficial similarities between high-levelprogramming languages and natural languages. These superficialsimilarities also may cause a glossing over of the fact that high-levelprogramming language implementations ultimately perform valuable work bycreating/controlling many different computational machines.

The many different computational machines that a high-level programminglanguage specifies are almost unimaginably complex. At base, thehardware used in the computational machines typically consists of sometype of ordered matter (e.g., traditional electronic devices (e.g.,transistors), deoxyribonucleic acid (DNA), quantum devices, mechanicalswitches, optics, fluidics, pneumatics, optical devices (e.g., opticalinterference devices), molecules, etc.) that are arranged to form logicgates. Logic gates are typically physical devices that may beelectrically, mechanically, chemically, or otherwise driven to changephysical state in order to create a physical reality of logic, such asBoolean logic.

Logic gates may be arranged to form logic circuits, which are typicallyphysical devices that may be electrically, mechanically, chemically, orotherwise driven to create a physical reality of certain logicalfunctions. Types of logic circuits include such devices as multiplexers,registers, arithmetic logic units (ALUs), computer memory, etc., eachtype of which may be combined to form yet other types of physicaldevices, such as a central processing unit (CPU)—the best known of whichis the microprocessor. A modern microprocessor will often contain morethan one hundred million logic gates in its many logic circuits (andoften more than a billion transistors). See, e.g., Wikipedia, Logicgates, http://en.wikipedia.org/wiki/Logic_gates (as of Jun. 5, 2012,21:03 GMT).

The logic circuits forming the microprocessor are arranged to provide amicroarchitecture that will carry out the instructions defined by thatmicroprocessor's defined Instruction Set Architecture. The InstructionSet Architecture is the part of the microprocessor architecture relatedto programming, including the native data types, instructions,registers, addressing modes, memory architecture, interrupt andexception handling, and external Input/Output. See, e.g., Wikipedia,Computer architecture,http://en.wikipedia.org/wiki/Computer_architecture (as of Jun. 5, 2012,21:03 GMT).

The Instruction Set Architecture includes a specification of the machinelanguage that can be used by programmers to use/control themicroprocessor. Since the machine language instructions are such thatthey may be executed directly by the microprocessor, typically theyconsist of strings of binary digits, or bits. For example, a typicalmachine language instruction might be many bits long (e.g., 32, 64, or128 bit strings are currently common). A typical machine languageinstruction might take the form “11110000101011110000111100111111” (a 32bit instruction).

It is significant here that, although the machine language instructionsare written as sequences of binary digits, in actuality those binarydigits specify physical reality. For example, if certain semiconductorsare used to make the operations of Boolean logic a physical reality, theapparently mathematical bits “1” and “0” in a machine languageinstruction actually constitute a shorthand that specifies theapplication of specific voltages to specific wires. For example, in somesemiconductor technologies, the binary number “1” (e.g., logical “1”) ina machine language instruction specifies around +5 volts applied to aspecific “wire” (e.g., metallic traces on a printed circuit board) andthe binary number “0” (e.g., logical “0”) in a machine languageinstruction specifies around −5 volts applied to a specific “wire.” Inaddition to specifying voltages of the machines' configurations, suchmachine language instructions also select out and activate specificgroupings of logic gates from the millions of logic gates of the moregeneral machine. Thus, far from abstract mathematical expressions,machine language instruction programs, even though written as a stringof zeros and ones, specify many, many constructed physical machines orphysical machine states.

Machine language is typically incomprehensible by most humans (e.g., theabove example was just ONE instruction, and some personal computersexecute more than two billion instructions every second). See, e.g.,Wikipedia, Instructions per second,http://en.wikipedia.org/wiki/Instructions_per_second (as of Jun. 5,2012, 21:04 GMT). Thus, programs written in machine language—which maybe tens of millions of machine language instructions long—areincomprehensible to most humans. In view of this, early assemblylanguages were developed that used mnemonic codes to refer to machinelanguage instructions, rather than using the machine languageinstructions' numeric values directly (e.g., for performing amultiplication operation, programmers coded the abbreviation “mult,”which represents the binary number “011000” in MIPS machine code). Whileassembly languages were initially a great aid to humans controlling themicroprocessors to perform work, in time the complexity of the work thatneeded to be done by the humans outstripped the ability of humans tocontrol the microprocessors using merely assembly languages.

At this point, it was noted that the same tasks needed to be done overand over, and the machine language necessary to do those repetitivetasks was the same. In view of this, compilers were created. A compileris a device that takes a statement that is more comprehensible to ahuman than either machine or assembly language, such as “add 2+2 andoutput the result,” and translates that human understandable statementinto a complicated, tedious, and immense machine language code (e.g.,millions of 32, 64, or 128 bit length strings). Compilers thus translatehigh-level programming language into machine language.

This compiled machine language, as described above, is then used as thetechnical specification which sequentially constructs and causes theinteroperation of many different computational machines such thatuseful, tangible, and concrete work is done. For example, as indicatedabove, such machine language—the compiled version of the higher-levellanguage—functions as a technical specification which selects outhardware logic gates, specifies voltage levels, voltage transitiontimings, etc., such that the useful work is accomplished by thehardware.

Thus, a functional/operational technical description, when viewed by oneof skill in the art, is far from an abstract idea. Rather, such afunctional/operational technical description, when understood throughthe tools available in the art such as those just described, is insteadunderstood to be a humanly understandable representation of a hardwarespecification, the complexity and specificity of which far exceeds thecomprehension of most any one human. With this in mind, those skilled inthe art will understand that any such operational/functional technicaldescriptions—in view of the disclosures herein and the knowledge ofthose skilled in the art—may be understood as operations made intophysical reality by (a) one or more interchained physical machines, (b)interchained logic gates configured to create one or more physicalmachine(s) representative of sequential/combinatorial logic(s), (c)interchained ordered matter making up logic gates (e.g., interchainedelectronic devices (e.g., transistors), DNA, quantum devices, mechanicalswitches, optics, fluidics, pneumatics, molecules, etc.) that createphysical reality of logic(s), or (d) virtually any combination of theforegoing. Indeed, any physical object which has a stable, measurable,and changeable state may be used to construct a machine based on theabove technical description. Charles Babbage, for example, constructedthe first mechanized computational apparatus out of wood, with theapparatus powered by cranking a handle.

Thus, far from being understood as an abstract idea, those skilled inthe art will recognize a functional/operational technical description asa humanly-understandable representation of one or more almostunimaginably complex and time sequenced hardware instantiations. Thefact that functional/operational technical descriptions might lendthemselves readily to high-level computing languages (or high-levelblock diagrams for that matter) that share some words, structures,phrases, etc. with natural language should not be taken as an indicationthat such functional/operational technical descriptions are abstractideas, or mere expressions of abstract ideas. In fact, as outlinedherein, in the technological arts this is simply not true. When viewedthrough the tools available to those of skill in the art, suchfunctional/operational technical descriptions are seen as specifyinghardware configurations of almost unimaginable complexity.

As outlined above, the reason for the use of functional/operationaltechnical descriptions is at least twofold. First, the use offunctional/operational technical descriptions allows near-infinitelycomplex machines and machine operations arising from interchainedhardware elements to be described in a manner that the human mind canprocess (e.g., by mimicking natural language and logical narrativeflow). Second, the use of functional/operational technical descriptionsassists the person of skill in the art in understanding the describedsubject matter by providing a description that is more or lessindependent of any specific vendor's piece(s) of hardware.

The use of functional/operational technical descriptions assists theperson of skill in the art in understanding the described subject mattersince, as is evident from the above discussion, one could easily,although not quickly, transcribe the technical descriptions set forth inthis document as trillions of ones and zeroes, billions of single linesof assembly-level machine code, millions of logic gates, thousands ofgate arrays, or any number of intermediate levels of abstractions.However, if any such low-level technical descriptions were to replacethe present technical description, a person of skill in the art couldencounter undue difficulty in implementing the disclosure, because sucha low-level technical description would likely add complexity without acorresponding benefit (e.g., by describing the subject matter utilizingthe conventions of one or more vendor-specific pieces of hardware).Thus, the use of functional/operational technical descriptions assiststhose of skill in the art by separating the technical descriptions fromthe conventions of any vendor-specific piece of hardware.

In view of the foregoing, the logical operations/functions set forth inthe present technical description are representative of static orsequenced specifications of various ordered-matter elements, in orderthat such specifications may be comprehensible to the human mind andadaptable to create many various hardware configurations. The logicaloperations/functions disclosed herein should be treated as such, andshould not be disparagingly characterized as abstract ideas merelybecause the specifications they represent are presented in a manner thatone of skill in the art can readily understand and apply in a mannerindependent of a specific vendor's hardware implementation.

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware, software (e.g., a high-level computer program servingas a hardware specification), and/or firmware implementations of aspectsof systems; the use of hardware, software, and/or firmware is generally(but not always, in that in certain contexts the choice between hardwareand software can become significant) a design choice representing costvs. efficiency tradeoffs. Those having skill in the art will appreciatethat there are various vehicles by which processes and/or systems and/orother technologies described herein can be effected (e.g., hardware,software (e.g., a high-level computer program serving as a hardwarespecification), and/or firmware), and that the preferred vehicle willvary with the context in which the processes and/or systems and/or othertechnologies are deployed. For example, if an implementer determinesthat speed and accuracy are paramount, the implementer may opt for amainly hardware and/or firmware vehicle; alternatively, if flexibilityis paramount, the implementer may opt for a mainly software (e.g., ahigh-level computer program serving as a hardware specification)implementation; or, yet again alternatively, the implementer may opt forsome combination of hardware, software (e.g., a high-level computerprogram serving as a hardware specification), and/or firmware in one ormore machines, compositions of matter, and articles of manufacture,limited to patentable subject matter under 35 USC 101. Hence, there areseveral possible vehicles by which the processes and/or devices and/orother technologies described herein may be effected, none of which isinherently superior to the other in that any vehicle to be utilized is achoice dependent upon the context in which the vehicle will be deployedand the specific concerns (e.g., speed, flexibility, or predictability)of the implementer, any of which may vary. Those skilled in the art willrecognize that optical aspects of implementations will typically employoptically-oriented hardware, software (e.g., a high-level computerprogram serving as a hardware specification), and or firmware.

In some implementations described herein, logic and similarimplementations may include computer programs or other controlstructures. Electronic circuitry, for example, may have one or morepaths of electrical current constructed and arranged to implementvarious functions as described herein. In some implementations, one ormore media may be configured to bear a device-detectable implementationwhen such media hold or transmit device detectable instructions operableto perform as described herein. In some variants, for example,implementations may include an update or modification of existingsoftware (e.g., a high-level computer program serving as a hardwarespecification) or firmware, or of gate arrays or programmable hardware,such as by performing a reception of or a transmission of one or moreinstructions in relation to one or more operations described herein.Alternatively or additionally, in some variants, an implementation mayinclude special-purpose hardware, software (e.g., a high-level computerprogram serving as a hardware specification), firmware components,and/or general-purpose components executing or otherwise invokingspecial-purpose components. Specifications or other implementations maybe transmitted by one or more instances of tangible transmission mediaas described herein, optionally by packet transmission or otherwise bypassing through distributed media at various times.

Alternatively or additionally, implementations may include executing aspecial-purpose instruction sequence or invoking circuitry for enabling,triggering, coordinating, requesting, or otherwise causing one or moreoccurrences of virtually any functional operation described herein. Insome variants, operational or other logical descriptions herein may beexpressed as source code and compiled or otherwise invoked as anexecutable instruction sequence. In some contexts, for example,implementations may be provided, in whole or in part, by source code,such as C++, or other code sequences. In other implementations, sourceor other code implementation, using commercially available and/ortechniques in the art, may be compiled//implemented/translated/convertedinto a high-level descriptor language (e.g., initially implementingdescribed technologies in C or C++ programming language and thereafterconverting the programming language implementation into alogic-synthesizable language implementation, a hardware descriptionlanguage implementation, a hardware design simulation implementation,and/or other such similar mode(s) of expression). For example, some orall of a logical expression (e.g., computer programming languageimplementation) may be manifested as a Verilog-type hardware description(e.g., via Hardware Description Language (HDL) and/or Very High SpeedIntegrated Circuit Hardware Descriptor Language (VHDL)) or othercircuitry model which may then be used to create a physicalimplementation having hardware (e.g., an Application Specific IntegratedCircuit). Those skilled in the art will recognize how to obtain,configure, and optimize suitable transmission or computational elements,material supplies, actuators, or other structures in light of theseteachings.

The term module, as used in the foregoing/following disclosure, mayrefer to a collection of one or more components that are arranged in aparticular manner, or a collection of one or more general-purposecomponents that may be configured to operate in a particular manner atone or more particular points in time, and/or also configured to operatein one or more further manners at one or more further times. Forexample, the same hardware, or same portions of hardware, may beconfigured/reconfigured in sequential/parallel time(s) as a first typeof module (e.g., at a first time), as a second type of module (e.g., ata second time, which may in some instances coincide with, overlap, orfollow a first time), and/or as a third type of module (e.g., at a thirdtime which may, in some instances, coincide with, overlap, or follow afirst time and/or a second time), etc. Reconfigurable and/orcontrollable components (e.g., general purpose processors, digitalsignal processors, field programmable gate arrays, etc.) are capable ofbeing configured as a first module that has a first purpose, then asecond module that has a second purpose and then, a third module thathas a third purpose, and so on. The transition of a reconfigurableand/or controllable component may occur in as little as a fewnanoseconds, or may occur over a period of minutes, hours, or days.

In some such examples, at the time the component is configured to carryout the second purpose, the component may no longer be capable ofcarrying out that first purpose until it is reconfigured. A componentmay switch between configurations as different modules in as little as afew nanoseconds. A component may reconfigure on-the-fly, e.g., thereconfiguration of a component from a first module into a second modulemay occur just as the second module is needed. A component mayreconfigure in stages, e.g., portions of a first module that are nolonger needed may reconfigure into the second module even before thefirst module has finished its operation. Such reconfigurations may occurautomatically, or may occur through prompting by an external source,whether that source is another component, an instruction, a signal, acondition, an external stimulus, or similar.

For example, a central processing unit of a personal computer may, atvarious times, operate as a module for displaying graphics on a screen,a module for writing data to a storage medium, a module for receivinguser input, and a module for multiplying two large prime numbers, byconfiguring its logical gates in accordance with its instructions. Suchreconfiguration may be invisible to the naked eye, and in someembodiments may include activation, deactivation, and/or re-routing ofvarious portions of the component, e.g., switches, logic gates, inputs,and/or outputs. Thus, in the examples found in the foregoing/followingdisclosure, if an example includes or recites multiple modules, theexample includes the possibility that the same hardware may implementmore than one of the recited modules, either contemporaneously or atdiscrete times or timings. The implementation of multiple modules,whether using more components, fewer components, or the same number ofcomponents as the number of modules, is merely an implementation choiceand does not generally affect the operation of the modules themselves.Accordingly, it should be understood that any recitation of multiplediscrete modules in this disclosure includes implementations of thosemodules as any number of underlying components, including, but notlimited to, a single component that reconfigures itself over time tocarry out the functions of multiple modules, and/or multiple componentsthat similarly reconfigure, and/or special purpose reconfigurablecomponents.

Those skilled in the art will recognize that it is common within the artto implement devices and/or processes and/or systems, and thereafter useengineering and/or other practices to integrate such implemented devicesand/or processes and/or systems into more comprehensive devices and/orprocesses and/or systems. That is, at least a portion of the devicesand/or processes and/or systems described herein can be integrated intoother devices and/or processes and/or systems via a reasonable amount ofexperimentation. Those having skill in the art will recognize thatexamples of such other devices and/or processes and/or systems mightinclude—as appropriate to context and application—all or part of devicesand/or processes and/or systems of (a) an air conveyance (e.g., anairplane, rocket, helicopter, etc.), (b) a ground conveyance (e.g., acar, truck, locomotive, tank, armored personnel carrier, etc.), (c) abuilding (e.g., a home, warehouse, office, etc.), (d) an appliance(e.g., a refrigerator, a washing machine, a dryer, etc.), (e) acommunications system (e.g., a networked system, a telephone system, aVoice over IP system, etc.), (f) a business entity (e.g., an InternetService Provider (ISP) entity such as Comcast Cable, Qwest, SouthwesternBell, etc.), or (g) a wired/wireless services entity (e.g., Sprint,Cingular, Nextel, etc.), etc.

In certain cases, use of a system or method may occur in a territoryeven if components are located outside the territory. For example, in adistributed computing context, use of a distributed computing system mayoccur in a territory even though parts of the system may be locatedoutside of the territory (e.g., relay, server, processor, signal-bearingmedium, transmitting computer, receiving computer, etc. located outsidethe territory).

A sale of a system or method may likewise occur in a territory even ifcomponents of the system or method are located and/or used outside theterritory. Further, implementation of at least part of a system forperforming a method in one territory does not preclude use of the systemin another territory

In a general sense, those skilled in the art will recognize that thevarious embodiments described herein can be implemented, individuallyand/or collectively, by various types of electro-mechanical systemshaving a wide range of electrical components such as hardware, software,firmware, and/or virtually any combination thereof, limited topatentable subject matter under 35 U.S.C. 101; and a wide range ofcomponents that may impart mechanical force or motion such as rigidbodies, spring or torsional bodies, hydraulics, electro-magneticallyactuated devices, and/or virtually any combination thereof.Consequently, as used herein “electro-mechanical system” includes, butis not limited to, electrical circuitry operably coupled with atransducer (e.g., an actuator, a motor, a piezoelectric crystal, a MicroElectro Mechanical System (MEMS), etc.), electrical circuitry having atleast one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, electrical circuitry forming ageneral purpose computing device configured by a computer program (e.g.,a general purpose computer configured by a computer program which atleast partially carries out processes and/or devices described herein,or a microprocessor configured by a computer program which at leastpartially carries out processes and/or devices described herein),electrical circuitry forming a memory device (e.g., forms of memory(e.g., random access, flash, read only, etc.)), electrical circuitryforming a communications device (e.g., a modem, communications switch,optical-electrical equipment, etc.), and/or any non-electrical analogthereto, such as optical or other analogs (e.g., graphene basedcircuitry). Those skilled in the art will also appreciate that examplesof electro-mechanical systems include but are not limited to a varietyof consumer electronics systems, medical devices, as well as othersystems such as motorized transport systems, factory automation systems,security systems, and/or communication/computing systems. Those skilledin the art will recognize that electro-mechanical as used herein is notnecessarily limited to a system that has both electrical and mechanicalactuation except as context may dictate otherwise.

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware,and/or any combination thereof can be viewed as being composed ofvarious types of “electrical circuitry.” Consequently, as used herein“electrical circuitry” includes, but is not limited to, electricalcircuitry having at least one discrete electrical circuit, electricalcircuitry having at least one integrated circuit, electrical circuitryhaving at least one application specific integrated circuit, electricalcircuitry forming a general purpose computing device configured by acomputer program (e.g., a general purpose computer configured by acomputer program which at least partially carries out processes and/ordevices described herein, or a microprocessor configured by a computerprogram which at least partially carries out processes and/or devicesdescribed herein), electrical circuitry forming a memory device (e.g.,forms of memory (e.g., random access, flash, read only, etc.)), and/orelectrical circuitry forming a communications device (e.g., a modem,communications switch, optical-electrical equipment, etc.). Those havingskill in the art will recognize that the subject matter described hereinmay be implemented in an analog or digital fashion or some combinationthereof.

Those skilled in the art will recognize that at least a portion of thedevices and/or processes described herein can be integrated into animage processing system. Those having skill in the art will recognizethat a typical image processing system generally includes one or more ofa system unit housing, a video display device, memory such as volatileor non-volatile memory, processors such as microprocessors or digitalsignal processors, computational entities such as operating systems,drivers, applications programs, one or more interaction devices (e.g., atouch pad, a touch screen, an antenna, etc.), control systems includingfeedback loops and control motors (e.g., feedback for sensing lensposition and/or velocity; control motors for moving/distorting lenses togive desired focuses). An image processing system may be implementedutilizing suitable commercially available components, such as thosetypically found in digital still systems and/or digital motion systems.

Those skilled in the art will recognize that at least a portion of thedevices and/or processes described herein can be integrated into a dataprocessing system. Those having skill in the art will recognize that adata processing system generally includes one or more of a system unithousing, a video display device, memory such as volatile or non-volatilememory, processors such as microprocessors or digital signal processors,computational entities such as operating systems, drivers, graphicaluser interfaces, and applications programs, one or more interactiondevices (e.g., a touch pad, a touch screen, an antenna, etc.), and/orcontrol systems including feedback loops and control motors (e.g.,feedback for sensing position and/or velocity; control motors for movingand/or adjusting components and/or quantities). A data processing systemmay be implemented utilizing suitable commercially available components,such as those typically found in data computing/communication and/ornetwork computing/communication systems.

Those skilled in the art will recognize that at least a portion of thedevices and/or processes described herein can be integrated into a motesystem. Those having skill in the art will recognize that a typical motesystem generally includes one or more memories such as volatile ornon-volatile memories, processors such as microprocessors or digitalsignal processors, computational entities such as operating systems,user interfaces, drivers, sensors, actuators, applications programs, oneor more interaction devices (e.g., an antenna USB ports, acoustic ports,etc.), control systems including feedback loops and control motors(e.g., feedback for sensing or estimating position and/or velocity;control motors for moving and/or adjusting components and/orquantities). A mote system may be implemented utilizing suitablecomponents, such as those found in mote computing/communication systems.Specific examples of such components entail such as Intel Corporation'sand/or Crossbow Corporation's mote components and supporting hardware,software, and/or firmware.

For the purposes of this application, “cloud” computing may beunderstood as described in the cloud computing literature. For example,cloud computing may be methods and/or systems for the delivery ofcomputational capacity and/or storage capacity as a service. The “cloud”may refer to one or more hardware and/or software components thatdeliver or assist in the delivery of computational and/or storagecapacity, including, but not limited to, one or more of a client, anapplication, a platform, an infrastructure, and/or a server The cloudmay refer to any of the hardware and/or software associated with aclient, an application, a platform, an infrastructure, and/or a server.For example, cloud and cloud computing may refer to one or more of acomputer, a processor, a storage medium, a router, a switch, a modem, avirtual machine (e.g., a virtual server), a data center, an operatingsystem, a middleware, a firmware, a hardware back-end, a softwareback-end, and/or a software application. A cloud may refer to a privatecloud, a public cloud, a hybrid cloud, and/or a community cloud. A cloudmay be a shared pool of configurable computing resources, which may bepublic, private, semi-private, distributable, scaleable, flexible,temporary, virtual, and/or physical. A cloud or cloud service may bedelivered over one or more types of network, e.g., a mobilecommunication network, and the Internet.

As used in this application, a cloud or a cloud service may include oneor more of infrastructure-as-a-service (“IaaS”), platform-as-a-service(“PaaS”), software-as-a-service (“SaaS”), and/or desktop-as-a-service(“DaaS”). As a non-exclusive example, IaaS may include, e.g., one ormore virtual server instantiations that may start, stop, access, and/orconfigure virtual servers and/or storage centers (e.g., providing one ormore processors, storage space, and/or network resources on-demand,e.g., EMC and Rackspace). PaaS may include, e.g., one or more softwareand/or development tools hosted on an infrastructure (e.g., a computingplatform and/or a solution stack from which the client can createsoftware interfaces and applications, e.g., Microsoft Azure). SaaS mayinclude, e.g., software hosted by a service provider and accessible overa network (e.g., the software for the application and/or the dataassociated with that software application may be kept on the network,e.g., Google Apps, SalesForce). DaaS may include, e.g., providingdesktop, applications, data, and/or services for the user over a network(e.g., providing a multi-application framework, the applications in theframework, the data associated with the applications, and/or servicesrelated to the applications and/or the data over the network, e.g.,Citrix). The foregoing is intended to be exemplary of the types ofsystems and/or methods referred to in this application as “cloud” or“cloud computing” and should not be considered complete or exhaustive.

One skilled in the art will recognize that the herein describedcomponents (e.g., operations), devices, objects, and the discussionaccompanying them are used as examples for the sake of conceptualclarity and that various configuration modifications are contemplated.Consequently, as used herein, the specific exemplars set forth and theaccompanying discussion are intended to be representative of their moregeneral classes. In general, use of any specific exemplar is intended tobe representative of its class, and the non-inclusion of specificcomponents (e.g., operations), devices, and objects should not be takenlimiting.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures may beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled,” to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable,” to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents, and/or wirelessly interactable, and/or wirelesslyinteracting components, and/or logically interacting, and/or logicallyinteractable components.

To the extent that formal outline headings are present in thisapplication, it is to be understood that the outline headings are forpresentation purposes, and that different types of subject matter may bediscussed throughout the application (e.g., device(s)/structure(s) maybe described under process(es)/operations heading(s) and/orprocess(es)/operations may be discussed under structure(s)/process(es)headings; and/or descriptions of single topics may span two or moretopic headings). Hence, any use of formal outline headings in thisapplication is for presentation purposes, and is not intended to be inany way limiting.

Throughout this application, examples and lists are given, withparentheses, the abbreviation “e.g.,” or both. Unless explicitlyotherwise stated, these examples and lists are merely exemplary and arenon-exhaustive. In most cases, it would be prohibitive to list everyexample and every combination. Thus, smaller, illustrative lists andexamples are used, with focus on imparting understanding of the claimterms rather than limiting the scope of such terms.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations are not expressly set forth herein for sakeof clarity.

One skilled in the art will recognize that the herein describedcomponents (e.g., operations), devices, objects, and the discussionaccompanying them are used as examples for the sake of conceptualclarity and that various configuration modifications are contemplated.Consequently, as used herein, the specific exemplars set forth and theaccompanying discussion are intended to be representative of their moregeneral classes. In general, use of any specific exemplar is intended tobe representative of its class, and the non-inclusion of specificcomponents (e.g., operations), devices, and objects should not be takenlimiting.

Although one or more users maybe shown and/or described herein, e.g., inFIG. 1, and other places, as a single illustrated figure, those skilledin the art will appreciate that one or more users may be representativeof one or more human users, robotic users (e.g., computational entity),and/or substantially any combination thereof (e.g., a user may beassisted by one or more robotic agents) unless context dictatesotherwise. Those skilled in the art will appreciate that, in general,the same may be said of “sender” and/or other entity-oriented terms assuch terms are used herein unless context dictates otherwise.

In some instances, one or more components may be referred to herein as“configured to,” “configured by,” “configurable to,” “operable/operativeto,” “adapted/adaptable,” “able to,” “conformable/conformed to,” etc.Those skilled in the art will recognize that such terms (e.g.“configured to”) generally encompass active-state components and/orinactive-state components and/or standby-state components, unlesscontext requires otherwise.

It is noted that “wearable computer” is used throughout thisspecification, and in the examples given, it is generally a wearablecomputer that captures images. However, this is merely for exemplarypurposes. The same systems may apply to conventional digital cameras,and any other camera, including security cameras, surveillance cameras,motor vehicle mounted cameras, road/traffic cameras, cameras atautomated teller machines, and the like.

Referring now to FIG. 1, in an embodiment, an entity, e.g., a user of aprivacy beacon, e.g., user 2105, e.g., a person, e.g., “Jules Caesar,”may be associated with a “Don't Capture Me” (hereinafter “DCM”) privacybeacon, e.g., DCM Beacon 2110. In an embodiment, a DCM beacon may beactive, e.g., may contain circuitry and be an active unit, e.g.,something wearable, e.g., on a piece of clothing, or on a ring, or on adrone associated with the user. In an embodiment, the DCM beacon may bepassive, e.g., it may be something that can be detected in theelectromagnetic spectrum, or can be otherwise detected but does notcontain any circuitry or advanced logic gates of its own. In anembodiment, the DCM beacon may be a combination of the two.

In an embodiment, a DCM beacon may be detectable by a machine or a humanbeing (e.g., a stop sign painted on a user's forehead may be a DCMbeacon). In an embodiment, a DCM beacon may be detectable by aparticular type of machine, structure, or filter, and may be otherwiseundetectable or difficult to detect through human senses. For example,in an embodiment, a DCM beacon may be seen using ultraviolet or infraredlight, or a DCM beacon may emit light outside the visible spectrum. Inan embodiment, a DCM beacon may be visible or detectable after a filteris applied, e.g., a DCM beacon may be visible after a red filter isapplied, or after a transformation is applied to a captured image, e.g.,a Fourier transformation.

In an embodiment, a DCM beacon may be detected optically. In anotherembodiment, a DCM beacon may be detected by sensing a different kind ofwave emitted by a DCM beacon, e.g., a wave in the nonvisibleelectromagnetic spectrum, a sound wave, an electromagnetic wave, and thelike. In an embodiment, a DCM beacon may use quantum entanglement (e.g.,through use of an entanglement-based protocol, among others).

In an embodiment, a DCM beacon may transmit data, e.g., a terms ofservice for the user (e.g., user 2105) for which the DCM beacon (e.g.,DCM beacon 2110) is associated or linked. In an embodiment, a DCM beaconmay be encoded with a location of data, e.g., a web address of a serverwhere terms of service for the user (e.g., user 2105) for which the DCMbeacon (e.g., DCM beacon 2110) is associated.

In an embodiment, a DCM beacon may be provided by a drone, of any size,e.g., nanometers to full-sized aircraft, that is associated with theuser.

In an embodiment, a DCM beacon may be provided by a piece of electronicsthat a user carries, e.g., a cellular telephone, tablet, watch, wearablecomputer, or otherwise.

In an embodiment, a DCM beacon may be embedded in the user, ingested bythe user, implanted in the user, taped to the skin of the user, or maybe engineered to grow organically in the user's body.

In an embodiment, a DCM beacon may be controlled by a magnetic field orother field emitted by a user, either through a user's regularelectromagnetic field or through a field generated by a device, local orremote, associated with the user.

Referring again to FIG. 1, in an embodiment, a different user, e.g., awearable computer user 3105, may have a wearable computer 3100. Awearable computer may be a pair of eyeglasses, a watch, jewelry,clothing, shoes, a piece of tape placed on the user's skin, it may beingested by the user or otherwise embedded into the user's body.Wearable computer 3100 may be a piece of electronics carried by a user3105. Wearable computer 3100 may not be a “wearable” computer in atraditional sense, but may be a laptop computer, tablet device, orsmartphone carried by a user. In an embodiment, wearable computer 3100may not be associated with a user at all, but may simply be a part of asurveillance system, e.g., a security camera, or a camera at anAutomated Teller Machine (“ATM”).

Wearable Computer that Captures the Image (FIGS. 1-I; 1-J, 1-N, 1-O).

Referring now to FIG. 1, e.g., FIG. 1-J, wearable computer 3100 mayinclude a wearable computer image capturing device 3110, e.g., a lens.Wearable computer image capturing device 3110 may include functionalityto capture images, e.g., an image sensor, e.g., a charge-coupled device(“CCM”) or a complementary metal-oxide semiconductor (“CMOS”), ananalog-to digital converter, and/or any other equipment used to convertlight into electrons. Wearable computer image capturing device 3110 maycapture the optical data, which may remain as light data, or may beconverted into electrons through an image sensor, as raw data. This rawdata, e.g., raw data 2200 may be captured by the optical image dataacquiring module 3120 of wearable computer 3100. Optical image dataacquiring module 3120 may be configured to acquire an image, e.g., animage of user 2105. As described above, a DCM beacon 2110 may beassociated with user 2105. In an embodiment, at this point in theoperation of wearable computer 3100, no processing has been performed onthe raw image data 2200.

Although not pictured here, wearable computer image capturing device3110 may also include circuitry to detect audio (e.g., a microphone)and/or video (e.g., the ability to capture frames above a certain rateof frames per second). This circuitry and its related explanation havebeen omitted to maintain simplicity of the drawing, however, throughthis application, “raw image data 2200” should be considered to alsopossibly include still pictures, video, and audio, in some embodiments.

Referring now to FIG. 1-I, in an embodiment, wearable computer 3100 thenmay transfer the raw/optical image data 2200 to an image path splittingmodule 3130. This splitting path may be optical, e.g., a set ofmirrors/lenses, for the case in which raw image data 2200 is still inoptical form, or digital, e.g., through use of known electrical signalsplitters. Image path splitting module 3130 may be implemented ashardware, software, or a combination thereof.

Referring again to FIG. 1, e.g., FIG. 1-I, in an embodiment, the north(upper) branch, as illustrated in FIG. 1, transmits the raw image data2200 to an image prior-to-processing encryption module 3150. Imageprior-to-processing encryption module 3150 may receive the raw imagedata 2200. From there, image prior-to-processing encryption module 3150may acquire an encryption key that is device-specific, e.g., wearablecomputer device specific encryption key 3182. In an embodiment, wearablecomputer device-specific encryption key 3182 may be stored in wearablecomputer device memory 3180, which also may include encrypted imagestorage 3184, and a wearable computer user-specific encryption key 3186.In another embodiment, device-specific encryption key 3182 may beretrieved from elsewhere, e.g., cloud storage. In another embodiment,device-specific encryption key 3182 may be generated in real time by thedevice. In another embodiment, device-specific encryption key 3182 maybe generated in real time by the device based on random user input(e.g., the last five words spoken by the device and recorded).

In an embodiment, image prior-to-processing encryption module 3150 maygenerate encrypted image data 2210. Encrypted image data 2210 may bestored in encrypted image storage 3184 of wearable computer devicememory 3180. In an embodiment, encrypted image data 2210 also may betransmitted to central server encrypted data and beacon metadatatransmission module 3170.

Referring again to FIG. 1-I and FIG. 1-N, in an embodiment, the south(lower) branch, as illustrated in FIG. 1, may transmit the raw imagedata 2200 to a DCM beacon detecting module 3140. In an embodiment, DCMbeacon detecting module 3140 may include one or more of optics-based DCMbeacon detecting module 3142, which may be configured to detect the DCMbeacon in an optical signal (e.g., light). In an embodiment, DCM beacondetecting module 3140 may include digital image processing-based DCMbeacon detecting module 3144, which may be configured to detect the DCMbeacon in a converted electron signal (e.g., data signal). In anembodiment, DCM beacon detecting module 3140 is configured to detect apresence or an absence of a DCM beacon, e.g., DCM beacon 2110,associated with the entity (e.g., user 2105, e.g., “Jules Caesar”),without performing any additional processing on the image, or releasingthe image for other portions of wearable computer 3100 to use. In anembodiment, for example, raw image data 2200 is not stored in devicememory of wearable computer 3100 in a form that is accessible to otherapplications and/or programs available to wearable computer 3100 orother computing devices that may communicate with wearable computer3100. For example, a user 3105 of wearable computer 3100 may not, atthis stage in processing, capture the raw data 2200 and upload it to asocial networking site, e.g., Facebook. In an embodiment, DCM beacondetecting module 3140 may be implemented in hardware, which may preventusers or third parties from bypassing the DCM beacon detecting module3140, without disassembling the device and physically altering thecircuit/logic.

Referring now to FIG. 1-N, in an embodiment, the DCM beacon detectingmodule 3140 may detect the DCM beacon 2110. For example, in theexemplary embodiment shown in FIG. 1, DCM beacon detecting module 3140may detect the DCM beacon 2110 that is associated with user 2105, e.g.,Jules Caesar. Thus, DCM beacon detecting module 3140 now knows to lockthe image data and prevent unencrypted image data from being accessed onthe device. Although not shown in this example, if the DCM beacon hadnot been found, then in an embodiment, the image data 2200 would havebeen released for use by the device, e.g., for uploading to socialnetwork or cloud storage, for example.

In an embodiment, the detected DCM beacon 2110 associated with JulesCaesar may be transmitted to DCM beacon metadata generating module 3160.DCM beacon metadata generating module 3160 may generate metadata basedon the detection of the beacon. The metadata may be as simple as “theimage data contains a privacy beacon,” e.g., Boolean data. In anembodiment, the metadata may be more complex, and may identify the userassociated with the privacy beacon, e.g., the metadata may describe “Aprivacy beacon associated with Jules Caesar has been found in the imagedata.” In another embodiment, the metadata may include the terms ofservice associated with the personality rights of Jules Caesar, anexample of which terms of service will be provided in more detailherein.

In an embodiment, the detected DCM beacon 2110 may be very simple (e.g.,optically detectable), and to obtain/generate metadata associated withthe detected DCM beacon 2110, DCM beacon metadata generating module 3160may include a DCM server contacting module 3162, which may contact oneor more entities to obtain more information regarding the DCM beacon2110. The DCM beacon metadata generating module 3160 may, in someembodiments, transmit the DCM beacon, or the image in which the DCMbeacon was captured, to the external entity, in order to obtain moreaccurate data. For example, the DCM server contacting module 3162 maycontact service term management server 5000, which may have DCM beaconregistry 5010, which will be discussed in more detail further herein.

In an embodiment, DCM beacon metadata generating module 3160 maygenerate the DCM beacon metadata 2230, and transfer DCM beacon metadata2230 to central server encrypted data and beacon metadata transmissionmodule 3170.

Referring again to FIG. 1, e.g., FIG. 1-I, central server encrypted dataand beacon metadata transmission module 3170 may receive the encryptedimage data 2210 and the DCM beacon metadata 2230 (e.g., see FIG. 1-N).In an embodiment, central server encrypted data and beacon metadatatransmission module 3170 may facilitate the transmission of encryptedimage data 2210 and DCM beacon metadata 2230 to a server, e.g., wearablecomputer encrypted data receipt and determination server 4000, whichwill be discussed in more detail herein. In an embodiment, centralserver encrypted data and beacon metadata transmission module 3170 mayinclude one or more of DCM beacon metadata transmission module 3172,which may be configured to transmit the DCM beacon metadata 2230, andencrypted data transmission module 3174, which may be configured totransmit the encrypted image data 2210.

Wearable Computer Server (FIGS. 1-H, 1-G)

Referring again to FIG. 1, e.g., FIG. 1-H, in an embodiment, a systemmay include a wearable computer server, e.g., wearable computerencrypted data receipt and determination server 4000. In an embodiment,a wearable computer server may be provided by a manufacturer of thewearable device 3100. In an embodiment, a wearable computer server maybe provided by a developer of one or more software applications for thewearable device 3100. In an embodiment, wearable computer server 4000may not have a direct relationship with wearable device 3100 prior toreceiving the encrypted image data and the DCM beacon metadata 2230, aswill be discussed in more detail herein. In an embodiment, a wearablecomputer server 4000 may be implemented at a home computer of a user,for example, and may communicate only with wearable devices that areassociated with that user. In another embodiment, a wearable computerserver 4000 may communicate with many wearable devices 3100, which mayor may not have some relationship. In an embodiment, wearable computerserver 4000 may communicate with one or more wearable devices 3100through use of a communication network, which may use any known form ofdevice communication. In an embodiment, wearable computer server 4000may be chosen by wearable device 3100, either due to proximity or due toone or more properties or characteristics of wearable computer server4000. In an embodiment, wearable computer server 4000 may be free toagree or disagree to process DCM beacon and image data received fromvarious wearable devices 3100. In an embodiment, wearable computerserver 4000 may be distributed across many computers and/or servers.

In an embodiment, wearable computer encrypted data receipt anddetermination server 4000 may include an encrypted data and beaconmetadata reception module 4100. Encrypted data and beacon metadatareception module 4100 may receive encrypted image data 2210 and DCMbeacon metadata 2230 from wearable computer 3100, e.g., central serverencrypted data and beacon metadata transmission module 3170. In anembodiment, encrypted data and beacon metadata reception module 4100 mayinclude a DCM beacon metadata reception module 4104. DCM beacon metadatareception module 4104 may be configured to acquire a privacy metadata,e.g., DCM beacon metadata 2230, corresponding to a detection of a DCMbeacon, e.g., DCM beacon 2110, in the one or more images captured by theimage capture device, e.g., wearable computer 3100. In an embodiment,encrypted data and beacon metadata reception module 4100 may includeencrypted data reception module 4102. In an embodiment, encrypted datareception module 4102 may be configured to acquire one or more of ablock of encrypted data corresponding to one or more images thatpreviously have been encrypted, e.g., encrypted image data 2210. In anembodiment, encrypted data module 4102 may transmit, or facilitate thetransmission of, encrypted image data 2210 to an entity that willperform a secondary detection of the privacy beacon, e.g., DCM beacondetection test duplicating server 4800, which will be discussed in moredetail further herein.

Referring again to FIG. 1-H, in an embodiment, encrypted data and beaconmetadata reception module 4100 may transmit the received DCM beaconmetadata to DCM beacon metadata reading module 4120. If the DCM beaconmetadata 2230 indicates that a DCM beacon was not found, then, in anembodiment, processing may transfer to module 4220, which will bediscussed in more detail further herein. In the example shown in FIG. 1,the DCM beacon 2110 associated with Jules Caesar was found, and the DCMbeacon metadata 2230 indicates this state to DCM beacon metadata readingmodule 4120.

Referring now to FIG. 1-G, in an embodiment, when the presence of theDCM beacon 2110 is determined through the DCM beacon metadata, e.g., DCMbeacon metadata 2230, then a DCM beacon TOS retrieval module 4122 mayretrieve term data from a location, which may be a remote location,e.g., a DCM beacon management server 5100, which will be discussed inmore detail further herein. In an embodiment, DCM beacon TOS retrievalmodule 4122 may retrieve term data that includes a terms of service thatspecifies one or more conditions in which the image containing the DCMbeacon 2110 may be used. In an embodiment, the TOS may also specify oneor more penalties for using the personality rights that may beassociated with the image, without acquiring permission or paying alicensing fee prior to releasing or utilizing the image. In anembodiment, the TOS also may include language forcing the entity thatviewed the privacy beacon to accept the TOS upon viewing of the beacon.The TOS will be described in more detail with respect to modules 5000and 5100.

Referring again to FIG. 1-G, in an embodiment, wearable computerencrypted data receipt and determination server 4000 also may include anencrypted data value calculation module 4130. Encrypted data valuecalculation module 4130 may use one or more algorithms or other methodsof inducing or deducing an estimate regarding how much advertising orother revenue may be garnered by using the images containing the entityassociated with the privacy beacon. For example, in an embodiment,encrypted data value calculation module 4130 may include a facialrecognition program to recognize the person or persons associated withthe beacon. In another embodiment, however, this may not be necessary,because the DCM beacon metadata and/or the ToS may identify the person.In an embodiment, encrypted data value calculation module 4130 may usevarious heuristics to calculate ad revenue, e.g., based on models usedby popular advertising methods, or based on prior releases of images ofthe person associated with the DCM beacon 2110. In an embodiment, module4130 may use social networking to acquire a focus group and test theimage on the focus group, in order to assist in revenue determination.For example, in the example shown in FIG. 1, the image in question is ofJules Caesar, who is the reclusive leader of the Roman Empire, and sothe ad revenue generated from having an actual picture of Jules Caesar,or a video of Jules Caesar drinking a mead-and-tonic, may have high netvalue.

Referring again to FIG. 1-G, in an embodiment, the ToS acquired from DCMbeacon TOS retrieval module 4122, and the encrypted data valuationcalculated from encrypted data value calculation module 4130 may be sentto release of encrypted data determination module 4140. Release ofencrypted data determination module 4140 may make a determination, atleast partly based on the acquired metadata, and at least partly basedon a value calculation based on the representation of the feature of theperson associated with the DCM beacon 2110 (e.g., Jules Caesar drinkinga mead-and-tonic). That determination may be regarding whether to allowan action, e.g., processing, decryption, distribution, editing,releasing, sharing, saving, posting to a social network, and the like,of the image. In an embodiment, the decision may be based on whether thepotential advertising revenue outweighs the potential damages retrievedfrom the terms of service. In an embodiment, this calculation may be astrict number comparison (e.g., is “revenue” greater than “damages”). Inan embodiment, the calculation may include more complex factors, e.g.,likelihood of success on a damages claim, likelihood that revenues willincrease, secondary revenue factors from increased traffic and/or brandawareness, and the like. In addition, in an embodiment, the comparisonmay not be strictly less than/greater than, e.g., in a risk adversealgorithm, if the numbers are close, then the determination may be tonot release the encrypted data, even if the potential ad revenue iscalculated as larger than the potential damages by a small amount.

Referring again to FIG. 1-G, if the determination made by release ofencrypted data determination module 4140 is “NO,” e.g., the potentialrevenue is less than the potential damages, then the encrypted data 2210is moved to an encrypted data holding and/or quarantine module 4150. Inan embodiment, the data from encrypted data holding and/or quarantinemodule 4150 is deleted after a predetermined time period, e.g., sevendays. In an embodiment, the data is simply stored, encrypted and lockedaway. In an embodiment, the encrypted image data 2210 may be transmittedto an ad replacement value determination server 4400, shown in FIG. 1-F,which will be discussed in more detail herein.

Referring again to FIG. 1-G, if the determination made by release ofencrypted data determination module 4140 is “YES,” e.g., the potentialrevenue is more than the potential damages, then the encrypted data 2210is transferred to encrypted data decryption enabling module 4152, shownin FIG. 1-H. In an embodiment, encrypted data decryption enabling module4152 may be configured to determine whether to perform decryption of atleast a portion of the encrypted data 2210 based on the result frommodule 4140 by transmitting the encrypted image data 2210 to wearablecomputer acquired encrypted data decryption and re-encryption server4200, which will be discussed in more detail.

Wearable Computer Acquired Encrypted Data Decryption and Re-EncryptionServer 4200 (FIGS. 1-L and 1-M)

Referring now to FIG. 1-M, in an embodiment, the system may includewearable computer acquired encrypted data decryption and re-encryptionserver 4200. In an embodiment, wearable computer acquired encrypted datadecryption and re-encryption server 4200 may be a portion of wearablecomputer server 4000. In an embodiment, however, wearable computeracquired encrypted data decryption and re-encryption server 4200 may bea different server than wearable computer server 4000, and may becontrolled by a different entity. For example, in an embodiment, theowner of the wearable computer 3100 hardware may control wearablecomputer server 4000. After the decision is made to decrypt the data atthe wearable computer server 4000, control may be handed off to adifferent server in control of software on the wearable computer, e.g.,software that handles pictures taken by the wearable computer 3100. Inanother embodiment, wearable computer acquired encrypted data decryptionand re-encryption server 4200 may be controlled by a socialnetworking/media site, e.g., Facebook, who may have an agreement toacquire the image data at the same time as the device.

Referring again to FIG. 1-M, in an embodiment, wearable computeracquired encrypted data decryption and re-encryption server 4200 mayinclude encrypted data acquiring module 4210, which may acquire theencrypted image data 2210 from the wearable computer server 4000. In anembodiment, wearable computer acquired encrypted data decryption andre-encryption server 4200 may include a privacy metadata acquiringmodule 4220, which may acquire privacy metadata from module 4120, if theDCM beacon was never detected and the image is free to be used. Forexample, in an embodiment, image data with no DCM beacon may be treatedsimilarly to image data with a DCM beacon, but that has been determinedto have an advertising value greater than a potential damages value. Forexample, in an embodiment, image data with no DCM beacon may be treatedas image data with potential damages value of zero.

Referring again to FIG. 1-M, in an embodiment, wearable computeracquired encrypted data decryption and re-encryption server 4200 mayinclude data indicating profitability of image with DCM beacon acquiringmodule 4230, which may receive data from module 4150 of wearablecomputer server 4000 indicating that the image should be decryptedregardless of the DCM beacon because of its potential profitability.

Referring again to FIG. 1-M, in an embodiment, wearable computeracquired encrypted data decryption and re-encryption server 4200 mayinclude image data decryption preparation module 4240, which may receivedata from one or more of data indicating profitability of image with DCMbeacon acquiring module 4230, encrypted data acquiring module 4210, andprivacy metadata acquiring module 4220. In an embodiment, module 4240may prepare the image or images for decryption, e.g., performpre-processing, check image integrity, reconfirm the privacy beaconcalculations, and the like.

Referring now to FIG. 1-L, wearable computer acquired encrypted datadecryption and re-encryption server 4200 may include device-specific keyretrieving module 4250 which may retrieve the device-specific key usedto encrypt/decrypt the encrypted image data 2210. In an embodiment,device-specific key retrieving module 4250 may include a device-specifickey retrieving from device module 4252, which may be configured toretrieve the device-specific key directly from the device that encryptedthe image, e.g., wearable computing device 3100. In an embodiment,device-specific key retrieving module 4250 may include a device-specifickey retrieving from server module 4254, which may be configured toretrieve the device-specific key from a server, e.g., from wearablecomputer encrypted data receipt and determination server 400, or fromDCM beacon detection test duplicating server 4800, or from anotherserver not depicted in FIG. 1.

Referring again to FIG. 1-L, in an embodiment, image data decryptionwith device-specific key module 4260 may take the device-specific keyretrieved from module 4250, and apply it to the encrypted image data2210 to generate decrypted image data 2280, as shown by the icon withthe unlocked lock in FIG. 1-L.

Referring again to FIG. 1-L, the image data has been decrypted. However,to protect security, in some embodiments, the data may be re-encryptedwith a key that is not tied to a specific device, but may be tied to aspecific user of the device, e.g., the key may be related to user 3105,rather than wearable device 3100. This embodiment will be described inmore detail herein. This embodiment allows the re-encrypted data to besecurely sent to a different device belonging to the user, e.g., a smartTV, a home computer, a video game system, or another portable electronicdevice, e.g., a cellular smartphone. In an embodiment, the re-encryptionwith a user specific key may be omitted.

In an embodiment, wearable computer acquired encrypted data decryptionand re-encryption server 4200 may include a user-specific key retrievingmodule 4270, that may be configured to obtain, through generation,acquisition, reception, or retrieval, of a user-specific encryption key.The user-specific encryption key may be delivered to image dataencrypting with user-specific key module 4280, which, in an embodiment,also may receive the decrypted image data 2280.

Referring again to FIG. 1-L, in an embodiment, image data encryptingwith user-specific key module 4280 may be configured to encrypt theblock of decrypted data through use of a unique user code that isrelated to the user 3105 of the wearable device 3100. Theagain-encrypted image data then may be transferred to encrypted imagedata transmitting module 4290. In an embodiment, encrypted image datatransmitting module 4290 may transmit the image data that has beenencrypted with a user-specific key to one or more other devices, whichwill be discussed in more detail herein.

Computing Device that Receives the Image Data (FIGS. 1-S and 1-T).

Referring now to FIG. 1-S, in an embodiment, the system may include acomputing device 3200, which may be a wearable computer or other device.In an embodiment, computing device 3200 may be the same as wearablecomputer 3100, but it does not necessarily have to be the same. In anembodiment, computing device 3200 receives the image data. In anembodiment, as described above, the received image data has beenencrypted with a user-specific code. Thus, in such an embodiment,computing device 3200 may be associated with user 3105 of the wearablecomputing device 3100. For example, a user 3105 may have a wearablecomputing device 3100 that captures images of people. After processingthose images at the server 4000, for example, the images, which, in someembodiments, now may be encrypted with a user-specific code, may betransmitted to computing device 3200, which may be the user 3105's homemedia center back at her house. In another embodiment, computing device3200 may be user 3105's laptop device, or user 3105's smartphone ortablet device. And, as previously mentioned, in another embodiment,computing device 3200 may simply be the user 3105's wearable computingdevice 3100 that captured the images originally.

In an embodiment, the computing device 3200 and the wearable computingdevice 3100 pictured in FIG. 1 are the same device. In an embodiment,the encryption, transmission to a server, decryption, and transmissionback, may occur invisibly to the user 3105, e.g., to the user 3105 ofthe wearable computing device 3100, the images are available to herafter they are recorded and saved, with a delay that is not specified.In some embodiments, the user 3105 may not be informed of the path takenby the captured image data.

In an embodiment, wearable computing device 3100 may include anencrypted image data receiving module 3210 configured to acquire thedata encrypted by the user-specific key code from encrypted image datatransmitting module 4290 of wearable computer 4200. In an embodiment,computing device 3200 may include image data release verificationacquiring module 3220, which may be configured to determine that theimages received from the encrypted image data transmitting module 4290of wearable computer 4200 have been approved for release and/or use. Inan embodiment, the determination may be made based on the ground thatthe images are encrypted with a user-specific key rather than a devicespecific key, if it is possible to tell from the encrypted information(e.g., in some embodiments, different types of encryption that may leavea different “signature” may be used). In an embodiment, thedetermination may be made by again analyzing the image data. In anembodiment, image data release verification acquiring module 3220 mayinclude encrypted image data analysis module 3222 which may performanalysis on the encrypted image data, including, but not limited to,reading metadata attached to the encrypted image data, to verify thatthe received encrypted image data is approved for release and/orprocessing. In an embodiment, image data release verification acquiringmodule 3220 may include release verification data retrieving module3224, which may be configured to obtain release verification data fromthe device that performed the verification, e.g., server 4000, or from adifferent device.

Referring now to FIG. 1-T, in an embodiment, computing device 3200 mayinclude device memory 3280. Device memory 3280 may store the wearablecomputer user-specific encryption/decryption key 3286, which may be usedto decrypt the received encrypted image data. In an embodiment, devicememory 3280 also may include encrypted image storage 3284, which mayinclude one or more image data, which may be encrypted.

Referring again to FIG. 1-S, in an embodiment, computing device 3200 mayinclude user-specific decryption key obtaining module 3230, which mayobtain the user-specific encryption/decryption key. In an embodiment,user-specific decryption key obtaining module 3230 may includeencryption/decryption key external source obtaining module 3232, whichmay be configured to obtain the encryption/decryption key from anexternal source, e.g., server 4000. In an embodiment, user-specificdecryption key obtaining module may include encryption/decryption keymemory retrieving module 3234, which may be configured to retrieve theencryption/decryption key from device memory 3280 of computing device3200.

Referring again to FIG. 1-S, in an embodiment, computing device 3200 mayinclude image decryption module 3240, which may use the user-specificencryption/decryption key to decrypt the image data. In an embodiment,the decrypted image data then may be sent to decrypted image releasemodule 3250, where the clear image data may be accessed by the device,and transmitted to other locations, posted to social networking or cloudstorage, be shared, manipulated, saved, edited, and otherwise have openaccess to the decrypted image data.

Ad Replacement Value Determination Server (FIG. 1-F).

Referring back to FIG. 1-G, as discussed briefly above, release ofencrypted data determination module 4140 may determine not to releasethe encrypted data, which may be stored in an encrypted data holdingand/or quarantine module 4150. In an embodiment, the encrypted data andthe DCM beacon may be transmitted to an ad replacement valuedetermination server, as shown in FIG. 1-F.

Referring now to FIG. 1-F, in an embodiment, the system may include anad replacement value determination server 4400. Ad replacement valuedetermination server 4400 may take the encrypted image data anddetermine if there is a way to monetize the images such that themonetization may outweigh the potential damages. For example, adreplacement value determination server 4400 may calculate potentialearnings and limited damages liability, if, for example, an entity withthe DCM beacon, e.g., Jules Caesar, is instead shown with anadvertisement where his head would normally be. In an embodiment, adreplacement value server may be controlled by a different entity thanserver 4000, and there may be an agreement in place for the adreplacement value determination server 4400 to receive encrypted datafor which the server 4000 decides it does not want to allowdistribution. For example, ad replacement value server 4400 may be runby a smaller social networking site that cares less about potentialdamages because they have fewer assets, or are less risk-averse. Inanother embodiment, ad replacement value determination server 4400 maybe part of server 4000, and it may be a practice of server 4000 to sendan encrypted image for further analysis after the server 4000 determinesthat the image is not likely to be profitable without modification.

Referring again to FIG. 1-F, in an embodiment, ad replacement valuedetermination server 4400 may include a DCM beacon metadata receptionmodule 4410 configured to receive the DCM beacon metadata from thewearable computer encrypted data receipt and determination server 4000.In an embodiment, ad replacement value determination server 4400 mayinclude an encrypted data reception module 4420 that may be configuredto receive the encrypted data from the wearable computer encrypted datareceipt and determination server 4000, e.g., from the encrypted dataholding module 4150.

Referring again to FIG. 1-F, in an embodiment, ad replacement valuedetermination server 4400 may include a DCM beacon term acquiring module4430, which may acquire one or more terms of service from service termmanagement server 5000 and/or DCM beacon management server 5100,similarly to DCM beacon terms-of-service retrieval module 4122 ofwearable computer encrypted data receipt and determination server 4000.In an embodiment, DCM beacon term acquiring module may include DCMbeacon remote retrieval module 4432. In an embodiment, DCM beacon termacquiring module may be configured to retrieve term data from a remotelocation, e.g., service term management server 5000, which term data maycorrespond to a term of service associated with a release of image datathat includes the person with which the DCM beacon is associated, e.g.,Jules Caesar.

Referring again to FIG. 1-F, in an embodiment, ad replacement valuedetermination server 4400 may include an encrypted data valuecalculation with standard ad placement module 4440. In an embodiment,standard ad placement module 4440 may perform a similar calculation asencrypted data value calculation module 4130 of wearable computerencrypted data receipt and determination server 4000. In an embodiment,for example, encrypted data value calculation with standard ad placementmodule 4440 may calculate whether an estimated advertising revenue fromone or more advertisement images placed in the encrypted image data willbe greater than an estimated potential liability for distribution of theimages. In an embodiment, the estimated potential liability is based atleast in part on the terms of service which may be retrieved by the DCMbeacon term acquiring module 4430.

Referring again to FIG. 1-F, in an embodiment, ad replacement valuedetermination server 4400 may include encrypted image data modificationwith intentionally obscuring ad placement module 4450. In an embodiment,encrypted image data modification with intentionally obscuring adplacement module 4450 may be configured to modify the encrypted imagedata (e.g., which, in some embodiments, may require limited decryptionand then re-encryption) by replacing one or more areas associated withthe entity related to the DCM beacon, e.g., Jules Caesar's face (e.g.,or in another embodiment, Jules Caesar's genitalia, if, e.g., it was anaked picture of Jules Caesar), with one or more advertisement images.

Referring again to FIG. 1-F, in an embodiment, ad replacement valuedetermination server 4400 may include modified encrypted data valuecalculation with intentionally obscuring ad placement module 4460. In anembodiment, modified encrypted data value calculation with intentionallyobscuring ad placement module 4460 may be configured to calculate anestimated advertising revenue from the modified image data. In anembodiment, the modified image data then may be distributed throughmodified encrypted data distributing module 4470.

Tracking Server (FIG. 1-E).

Referring now to FIG. 1-E, in an embodiment, a system may includetracking server 9000. Tracking server 9000 may be configured to log useof a “Don't Capture Me” (hereinafter “DCM”) beacon by one or multipleusers. In an embodiment, tracking server 9000 may track active DCMbeacons, e.g., beacon 2110, through communication with said one or morebeacons. In an embodiment, tracking server may track DCM beacons throughother means, e.g., social networking and the like. The DCM beacon doesnot need to be an active DCM beacon in order to be tracked by trackingserver 9000.

In an embodiment, tracking server 9000 may include deployment of one ormore active and/or passive DCM beacons monitoring module 9010.Deployment of one or more active and/or passive DCM beacons monitoringmodule 9010 may include one or more of active DCM beacon monitoringmodule 9012 and passive DCM beacon monitoring/data gathering module9020. In an embodiment, passive DCM beacon monitoring/data gatheringmodule 9020 may gather data about the passive DCM beacon by observingit, e.g., through satellite video capture, through other image capturingdevices, e.g., phone cameras, security cameras, laptop webcams, and thelike, or through other means. In an embodiment, passive DCM beaconmonitoring/data gathering module 9020 may include user input module9022, which may receive an indication from a user, e.g., a switchflipped on a user's cell phone, indicating that the user is using theDCM beacon. In an embodiment, passive DCM beacon monitoring/datagathering module 9020 may include a device status module which tracks adevice with which the passive DCM beacon is associated, e.g., a wearablecomputer that is a shirt, or a cellular phone device in the pocket. Inan embodiment, passive DCM beacon monitoring/data gathering module 9020may include a social media monitoring module that monitors posts onsocial networking sites to determine if the DCM beacon is being used,and a location of the user.

Referring again to FIG. 1-E, in an embodiment, tracking server 9000 mayinclude a record of the deployment of the one or more active and/orpassive DCM beacons storing module 9030, which may be configured tostore a record of usage and/or detection logs of the DCM beacons thatare monitored. In an embodiment, record of the deployment of the one ormore active and/or passive DCM beacons storing module 9030 may store arecord of the deployment in deployment record storage 9032. In anembodiment, record of the deployment of the one or more active and/orpassive DCM beacons storing module 9030 may transmit all or portions ofthe recorded record through record of the deployment of one or moreactive and/or passive DCM beacons transmitting module 9040.

Service Term Management Server 5000 (FIG. 1-A)

Referring now to FIG. 1-A, in an embodiment, the system may includeservice term management server 5000, which may manage terms of servicethat are associated with a DCM beacon and/or a person. In an embodiment,service term management server 5000 may include a DCM beacon registry5010. In an embodiment, the DCM beacon registry 5010 may include one ormore of a user's name, e.g., Jules Caesar, a terms of service associatedwith Jules Caesar, which may be custom to Jules Caesar, or may be ageneric terms of service that is used for many persons, and variousrepresentations of portions of Jules Caesar, e.g., likeness, handprint,footprint, voiceprint, pictures of private areas, and the like.

Referring again to FIG. 1-A, in an embodiment, the system may include aterms of service generating module 5020. Terms of service generatingmodule 5020 may create a terms of service for the user Jules Caesar. Asample Terms of Service is shown in FIG. 1-A and is reproduced here. Itis noted that this is a condensed Terms of Service meant to illustratean exemplary operation of the system in the environment, andaccordingly, several necessary legal portions may be omitted.Accordingly, the example Terms of Service should not be considered as abinding, legal document, but rather a representation of what thebinding, legal document would look like, that would enable one skilledin the art to create a full Terms of Service.

Exemplary Terms of Service for User 2105 (Jules Caesar)

1. By capturing an image of any part of the user Jules Caesar(hereinafter “Image”), or providing any automation, design, resource,assistance, or other facilitation in the capturing of the Image, youagree that you have captured these Terms of Service and that youacknowledge and agree to them. If you cannot agree to these Terms ofService, you should immediately delete the captured Image. Failure to doso will constitute acceptance of these Terms of Service.

2. The User Jules Caesar owns all of the rights associated with theImage and any representation of any part of Jules Caesar thereof;

3. By capturing the Image, you agree to provide the User Jules Caesarjust compensation for any commercialization of the User's personalityrights that may be captured in the Image.

4. By capturing the Image, you agree to take all reasonable actions totrack the Image and to provide an accounting of all commercializationattempts related to the Image, whether successful or not.

5. By capturing the Image, you accept a Liquidated Damages agreement inwhich unauthorized use of the Image will result in mandatory damages ofat least, but not limited to, $1,000,000.

In an embodiment, terms of service generating module may include one ormore of a default terms of service storage module 5022, a potentialdamage calculator 5024, and an entity interviewing for terms of servicegeneration module. In an embodiment, default terms of service storagemodule 5022 may store the default terms of service that are used as atemplate for a new user, e.g., when Jules Caesar signs up for theservice, this is the terms of service that is available to him. In anembodiment, potential damage calculator 5024 may determine an estimateof how much in damages that Jules Caesar could collect for a breach ofhis personality rights. In an embodiment, for example, potential damagecalculator may search the internet to determine how much Jules Caesarappears on social media, blogs, and microblog (e.g., Twitter) accounts.In an embodiment, entity interviewing for terms of service generationmodule 5026 may create an online questionnaire/interview for JulesCaesar to fill out, which will be used to calculate potential damages toJules Caesar, e.g., through determining Jules Caesar's net worth, forexample.

In an embodiment, service term management server 5000 may include termsof service maintenance module 5030, which may maintain the terms ofservice and modify them if, for example, the user becomes more popular,or gains a larger online or other presence. In an embodiment, terms ofservice maintenance module 5030 may include one or more of a socialmedia monitoring module 5042, that may search social networking sites,and an entity net worth tracking module 5034 that may have access to theentity's online bank accounts, brokerage accounts, property indexes,etc., and monitor the entity's wealth.

In an embodiment, serviced term management server 5000 may include a useof representations of an entity detecting module 5040. In an embodiment,use of representations of an entity detecting module 5040 may includeone or more of a social media monitoring module 5042, a public photorepository monitoring module 5044, and a public blog monitoring module5046. In an embodiment, use of representations of an entity detectingmodule 5040 may track uses of representations, e.g., images, of the userJules Caesar, to try to detect violations of the terms of service, invarious forums.

DCM Beacon Management Server 5100 (FIG. 1-C)

Referring now to FIG. 1-C, in an embodiment, the system may include aDCM beacon management server 5100, which may be configured to manage theDCM beacon associated with a user, e.g., DCM beacon 2110 for user 2105,e.g., Jules Caesar. In an embodiment, DCM beacon management server 5100and service term management server 5000 may be the same server. Inanother embodiment, DCM beacon management server 5100 and service termmanagement server 5000 may be hosted by different entities. For example,a specialized entity may handle the terms of service generation, e.g., avaluation company that may be able to determine a net “social network”worth of a user, e.g., Jules Caesar, and use that to fashion the termsof service.

Referring again to FIG. 1-C, in an embodiment, DCM beacon managementserver 5100 may include DCM beacon communication with entity wanting toavoid having their image captured module 5110. DCM beacon communicationwith entity wanting to avoid having their image captured module 5110 maybe configured to communicate with a user, e.g., user 2105, e.g., JulesCaesar, and may handle the creation, generation, maintenance, andproviding of the DCM beacon 2110 to Jules Caesar, whether throughelectronic delivery or through conventional delivery systems (e.g.,mail, pickup at a store, etc.). In an embodiment, DCM beaconcommunication with entity wanting to avoid having their image capturedmodule 5110 may include one or more of DCM beacon transmission module5112, DCM beacon receiving module 5114, and DCM beacon generating module5116.

In an embodiment, DCM beacon management server 5100 may include entityrepresentation acquiring module 5120. Entity representation acquiringmodule 5100 may be configured to receive data regarding one or morefeatures of the user that will be associated with the DCM beacon. Forexample, the user might upload pictures of his body, face, privateparts, footprint, handprint, voice recording, hairstyle, silhouette, orany other representation that may be captured and/or may be deemedrelevant.

In an embodiment, DCM beacon management server 5100 may include DCMbeacon association with one or more terms of service and one or moreentity representations module 5130. In an embodiment, DCM beaconassociation with one or more terms of service and one or more entityrepresentations module 5130 may be configured to, after generation of aDCM beacon, obtain a terms of service to be associated with that DCMbeacon. In an embodiment, the terms of service may be received fromservice term management server 5000.

In an embodiment, DCM beacon management server 5100 may include a DCMbeacon capture detecting module 5140. DCM beacon capture detectionmodule 5140 may detect when a DCM beacon is captured, e.g., if it is anactive beacon, or it may receive a notification from various servers(e.g., server 4000) and/or wearable devices (e.g., wearable device 3100)that a beacon has been detected, if it is a passive DCM beacon.

In an embodiment, when a DCM beacon is detected, DCM beacon managementserver 5100 may include terms of service associated with DCM beacondistributing module, which may be configured to provide the terms ofservice associated with the DCM beacon to an entity that captured theimage including the DCM beacon, e.g., to module 4122 of wearablecomputer encrypted data receipt and determination server 4000, or DCMbeacon remote retrieval module 4430 of ad replacement valuedetermination server 4400, for example.

Wearable Computer with Optional Paired Personal Device 3300 (FIGS. 1-Qand 1-R)

Referring now to FIG. 1-R, in an embodiment, the system may include awearable computer 3300. Wearable computer 3300 may have additionalfunctionality beyond capturing images, e.g., it may also store a user'scontact list for emails, phone calls, and the like. In anotherembodiment, wearable computer 3300 may be paired with another devicecarried by a user, e.g., the user's smartphone device, which stores theuser's contact list. As will be described in more detail herein,wearable computer 3300 operates similarly to wearable computer 3100,except that entities with DCM beacons are obscured, unless they have apreexisting relationship with the user. It is noted that DCM beacondetection and encryption may operate similarly in wearable computer 3300as in wearable computer 3100, and so substantially duplicated parts havebeen omitted.

Referring again to FIG. 1-R, in an embodiment, wearable computer 3300may include an image capturing module 3310, which may capture an imageof Jules Caesar, who has DCM beacon “A”, Beth Caesar, who has DCM beacon“B”, and Auggie Caesar, who has no DCM beacon. In an embodiment,wearable computer 3300 may include an image acquiring module 3320, whichmay be part of image capturing module 3310, to acquire one or moreimages captured by an image capture device, e.g., the image of JulesCaesar, Beth Caesar, and Auggie Caesar.

In an embodiment, wearable computer 3300 may include an entityidentification module 3330, which may perform one or more recognitionalgorithms on the image in order to identify persons in the image.Entity identification module may use known facial recognitionalgorithms, for example, or may ask the user for input, or may searchthe internet for similar images that have been identified, for example.

Referring again to FIG. 1-R, in an embodiment, wearable computer 3300may include preexisting relationship data retrieval module 3340, whichmay retrieve names of known persons, e.g., from a device contact list,e.g., device contact list 3350. In the example shown in FIG. 1, JulesCaesar is in the contact list of the device 3300. It is noted that thedevice contact list 3350 may be stored on a different device, e.g., theuser's cellular telephone.

Referring now to FIG. 1-Q, in an embodiment, wearable computer 3300 mayinclude data indicating an identified entity from the image data has apreexisting relationship obtaining module 3360, which, in an embodiment,may obtain data indicating that one of the entities recorded in theimage data (e.g., Jules Caesar) is in the user's contact list.

Referring again to FIG. 1-Q, in an embodiment, wearable computer 3300may include entities with preexisting relationship marking to preventobfuscation module 3370. In an embodiment, entities with preexistingrelationship marking to prevent obfuscation module 3370 may attach amarker to the image, e.g., a real marker on the image or a metadataattachment to the image, or another type of marker, that preventsobfuscation of that person, regardless of DCM beacon status, becausethey are in the user's contact list.

Referring again to FIG. 1-Q, in an embodiment, wearable computer 3300may include unknown entities with DCM beacon obscuring module 3380,which may obfuscate any of the entities in the image data that have aDCM beacon and are not in the contact list. For example, in the exampleshown in FIG. 1, Beth Caesar's image is obscured, e.g., blurred, blackedout, covered with advertisements, or the like, because she has a DCMbeacon associated with her image, and because she is not in the user'scontact list. Jules Caesar, on the other hand, is not obscured because aknown entity marker was attached to his image at module 3370, becauseJules Caesar is in the contact list of an associated device of the user.Auggie Caesar is not obscured regardless of contact list status, becausethere is no DCM beacon associated with Auggie Caesar.

Referring again to FIG. 1-Q, after the image is obscured, obscured image3390 of wearable computer 3300 may release the image to the rest of thedevice for processing, or to another device, the Internet, or cloudstorage, for further operations on the image data.

Active DCM Beacon 6000 (FIGS. 1-P and 1-K).

Referring now to FIG. 1-P, in an embodiment, a user 2107 may beassociated with an active DCM beacon 2610, which will be discussed inmore detail herein. The word “Active” in this context merely means thatthe DCM beacon has some form of circuitry or emitter.

Referring now to FIG. 1-K, in an embodiment, the system may include anactive DCM beacon 6000, which may show an active DCM beacon, e.g.,active DCM beacon 2610, in more detail. In an embodiment, beacon 6000may include DCM beacon broadcasting module 6010. In an embodiment, DCMbeacon broadcasting module 6010 may broadcast a privacy beaconassociated with at least one user, e.g., user 2107, from at or near thelocation of user 2107. The beacon may be detected by an image capturingdevice when the user is captured in an image.

Referring again to FIG. 1-K, in an embodiment, the beacon 6000 mayinclude an indication of DCM beacon detection module 6020, which maydetect, be informed of, or otherwise acquire an indication that theactive DCM beacon has been captured by an image capturing device. In anembodiment, indication of DCM beacon detection module 6020 may includeone or more of DCM beacon scanning module 6022, which may scan nearbydevices to see if they have detected the beacon, and DCM beaconcommunications handshake module 6024, which may establish communicationwith one or more nearby devices to determine if they have captured thebeacon.

Referring again to FIG. 1-K, in an embodiment, beacon 6000 may includeterm data broadcasting module 6030, which may broadcast, or which mayorder to be broadcasted, term data, which may include the terms ofservice. In an embodiment, term data broadcasting module 6030 mayinclude one or more of a substantive term data broadcasting module 6032,which may broadcast the actual terms of service, and pointer to termdata broadcasting module 6034, which may broadcast a pointer to theterms of service data that a capturing device may use to retrieve theterms of service from a particular location.

DCM Beacon Test Duplicating Sever 4800 (FIGS. 1-C and 1-D)

Referring now to FIG. 1-C, in an embodiment, the system may include aDCM beacon test duplicating server 4800. In an embodiment, the DCMbeacon test duplicating server 4800 may take the image data, and performthe test for capturing the beacon again, as a redundancy, as averification, or as a protection for wearable computer server 4000. Inan embodiment, DCM beacon test duplicating server 4800 may be a part ofwearable computer server 4000. In another embodiment, DCM beacon testduplicating server 4800 may be separate from wearable computer server4000, and may be controlled by a different entity, e.g., a watchdogentity, or an independent auditing agency.

Referring again to FIG. 1-C, in an embodiment, DCM beacon testduplicating server 4800 may include encrypted data reception forsecondary DCM beacon detection module 4810, which may acquire theencrypted image data containing the user, e.g., user 2105, e.g., JulesCaesar, and the associated DCM beacon, e.g., DCM beacon 2110.

Referring again to FIG. 1-C, in an embodiment, DCM beacon testduplicating server 4800 may include a device-specific key retrievingmodule 4820, which may retrieve the device-specific key, e.g., fromwearable computer device 3100, or from wearable computer server 4000. Inan embodiment, DCM beacon test duplicating server 4800 may include imagedata decryption with device-specific key module 4830, which may applythe device-specific key obtained by device-specific key retrievingmodule 4820, and apply it to the encrypted image data, to generatedecrypted image data.

Referring again to FIG. 1-C, in an embodiment, the unencrypted imagedata may be sent to DCM beacon detecting module 4840 of DCM beacon testduplicating server 4800. If the raw image data was optical in itsoriginal form, then it may be reconverted to optical (e.g., light) data.In an embodiment, DCM beacon detecting module 4840 may perform adetection for the DCM beacon, as previously described. In an embodiment,DCM beacon detecting module 4840 may include one or more of anoptics-based DCM beacon detecting module 4842 and a digital imageprocessing-based DCM beacon detecting module 4844.

Referring now to FIG. 1-D, after the test for detecting the DCM beacon2220 (which may be the same as the DCM beacon 2210, but is detected at adifferent place, so a different number has been assigned), DCM beacondetection at duplicating sever result obtaining module 4850 may obtainthe result of the detection performed at DCM beacon test duplicatingserver 4800. Similarly, DCM beacon detection at device result obtainingmodule 4860 may obtain the result from the DCM beacon detectionperformed at wearable computer device 3100. The results from module 4850and 4860 may be stored at DCM beacon test result storage and loggingmodule 4870 of DCM beacon test duplicating server 4800.

Referring again to FIG. 1-D, the test results from DCM beacon testduplicating server 4800 and from wearable computer 3100 may be stored atDCM beacon test result storage and logging module 4870, and such resultsmay be kept for a predetermined length of time. In an embodiment, theresults may be transmitted to a requesting party using DCM beacon testresult transmitting module 4880.

Referring again to the system, in an embodiment, acomputationally-implemented method may include acquiring an image, saidimage including at least one representation of a feature of at least oneentity, detecting a presence of a privacy beacon associated with the atleast one entity from the acquired image, without performance of afurther process on the acquired image, encrypting the image using aunique device code prior to performance of one or more image processesother than privacy beacon detection, said unique device code unique toan image capture device and not transmitted from the image capturedevice, and facilitating transmission of the encrypted image and privacybeacon data associated with the privacy beacon to a location configuredto perform processing on one or more of the encrypted image and theprivacy beacon data.

Referring again to the system, in an embodiment, acomputationally-implemented method may include acquiring a block ofencrypted data corresponding to one or more images that have previouslybeen encrypted through use of a unique device code associated with animage capture device configured to capture the one or more images,wherein at least one of the one or more images includes at least onerepresentation of a feature of at least one entity, acquiring a privacymetadata, said privacy metadata corresponding to a detection of aprivacy beacon in the one or more images captured by the image capturedevice, said privacy beacon associated with the at least one entity, anddetermining, at least partly based on the acquired privacy metadata, andpartly based on a value calculation based on the representation of thefeature of the at least one entity for which the privacy beacon isassociated, whether to allow processing, which may include distribution,decryption, etc., of the encrypted data block.

Referring again to the system, in an embodiment, acomputationally-implemented method may include acquiring a block ofencrypted data corresponding to one or more images that have previouslybeen encrypted through use of a unique device code associated with animage capture device configured to capture the one or more images,wherein at least one of the one or more images includes at least onerepresentation of a feature of at least one entity, acquiring a privacymetadata indicating detection of a privacy beacon in the one or moreimages captured by the image capture device, said privacy beaconassociated with the at least one entity, retrieving term data from aremote location, said term data corresponding to a term of serviceassociated with a potential release of the block of encrypted datacorresponding to the one or more images that have previously beenencrypted through use of the unique device code associated with theimage capture device configured to capture the one or more images,calculating an expected valuation corresponding to potential revenueassociated with the release of at least a portion of the block ofencrypted data corresponding to the one or more images that havepreviously been encrypted through use of the unique device codeassociated with the image capture device configured to capture the oneor more images, and determining whether to perform decryption of atleast a portion of the block of encrypted data at least partially basedon the calculation of the expected valuation corresponding to thepotential revenue associated with the release of the at least theportion of the block of encrypted data, and at least partially based onthe retrieved term data corresponding to the term of service.

Referring again to the system, in an embodiment, acomputationally-implemented method may include acquiring a block ofencrypted data corresponding to one or more images that have previouslybeen encrypted through use of a unique device code associated with animage capture device configured to capture the one or more images,wherein at least one of the one or more images includes at least onerepresentation of a feature of at least one entity, acquiring a privacymetadata indicating a lack of detection of a privacy beacon in the oneor more images captured by the image capture device, decrypting theblock of encrypted data corresponding to the one or more images thathave previously been encrypted through use of a unique device codeassociated with the image capture device, and encrypting the block ofdecrypted data through use of a unique entity code that is related to anentity associated with the image capture device configured to capturethe one or more images. Referring again to the system, in an embodiment,a computationally-implemented method may include acquiring a block ofencrypted data from a remote location, said block of encrypted datacorresponding to one or more images captured by an image capture device,said block of encrypted data previously encrypted through use of aunique entity code that is related to an entity associated with theimage capture device, receiving an indication that the one or moreimages captured by the image capture device were approved for decryptionthrough a verification related to privacy metadata associated with theone or more images, obtaining the unique entity code related to theentity associated with the image capture device, and releasing the oneor more images through decryption of the block of encrypted dataacquired from the remote location using the obtained unique entity coderelated to the entity associated with the image capture device.

Referring again to the system, in an embodiment, acomputationally-implemented method may include acquiring a block ofencrypted data corresponding to one or more images that have previouslybeen encrypted through use of a unique device code associated with animage capture device configured to capture the one or more images,wherein at least one of the one or more images includes at least onerepresentation of a feature of at least one entity, retrieving term datafrom a remote location, said term data corresponding to a term ofservice associated with a potential release of the one or more imagesthat have previously been encrypted through use of the unique devicecode associated with the image capture device configured to capture theone or more images, calculating whether an estimated advertising revenuefrom one or more advertisement images placed in the one or more imagesof the block of encrypted data will be greater than an estimatedpotential liability for distribution of the one or more images of theblock of encrypted data, said estimated potential liability at leastpartly based on the retrieved term data, modifying the one or moreimages of the block of encrypted data by replacing one or more areasassociated with one or more entities at least partially depicted in theone or more images with the one or more advertisement images, andcalculating a modified estimated advertising revenue from the modifiedone or more images of the block of encrypted data.

Referring again to the system, in an embodiment, acomputationally-implemented method may include monitoring a deploymentof a privacy beacon associated with a user, said privacy beaconconfigured to alert a wearable computer of one or more terms of serviceassociated with said user in response to recordation of image data thatincludes said privacy beacon by said wearable computer, and said privacybeacon configured to instruct said wearable computer to execute one ormore processes to impede transmission of the one or more images thatinclude the user associated with said privacy beacon, and storing arecord of the deployment of the privacy beacon associated with the user,said record configured to be retrieved upon request to confirm whetherthe privacy beacon associated with the user was active at a particulartime.

Referring again to the system, in an embodiment, acomputationally-implemented method may include receiving data regardingone or more features of one or more entities that are designated forprotection by one or more terms of service, associating the one or moreterms of service with a privacy beacon configured to be captured in animage when the one or more features of the one or more entities arecaptured in the image, and providing the terms of service to one or moremedia service providers associated with a device that captured an imagethat includes the privacy beacon, in response to receipt of anindication that an image that includes the privacy beacon has beencaptured.

Referring again to the system, in an embodiment, acomputationally-implemented method may include acquiring one or moreimages that have previously been captured by an image capture device,wherein at least one of the one or more images includes at least onerepresentation of a feature of one or more entities, identifying a firstentity for which at least one representation of a first entity featureis present in the one or more images, and a second entity for which atleast one representation of a second entity feature is present in theone or more images, obtaining data indicating that the first entity hasa preexisting relationship with an entity associated with the imagecapture device, e.g., in a contact list, preventing an obfuscation ofthe representation of the first entity for which the preexistingrelationship with the entity associated with the image capture devicehas been indicated, and obfuscating the representation of the secondentity for which at least one representation of the second entityfeature is present in the one or more images.

Referring again to the system, in an embodiment, acomputationally-implemented method may include broadcasting a privacybeacon associated with at least one entity from a location of the atleast one entity, said privacy beacon configured to be detected by animage capturing device upon capture of an image of the at least oneentity, acquiring an indication that the privacy beacon associated withthe at least one entity has been captured by the image capturing device,and broadcasting term data including one or more conditions and/orconsequences of distribution of one or more images that depict at leasta portion of the at least one entity.

Referring again to the system, in an embodiment, acomputationally-implemented method may include acquiring a block ofencrypted data corresponding to one or more images that have previouslybeen encrypted through use of a unique device code associated with animage capture device configured to capture the one or more images,wherein at least one of the one or more images includes at least onerepresentation of a feature of at least one entity, decrypting the blockof encrypted data corresponding to the one or more images that havepreviously been encrypted through use of the unique device codeassociated with the image capture device configured to capture the oneor more images, performing an operation to detect a presence of aprivacy beacon associated with the at least one entity from the one ormore images, wherein the privacy beacon previously had been detected bythe image capture device, and storing outcome data corresponding anoutcome of the operation to detect the presence of the privacy beaconassociated with the at least one entity of the one or more images,wherein said outcome data includes an indication of whether a result ofthe performed operation to detect the presence of the privacy beaconassociated with the at least one entity from the one or more imagesmatches the previous detection of the privacy beacon by the imagecapture device.

Referring now to FIG. 2, e.g., FIG. 2A, FIG. 2A illustrates an exampleenvironment 200 in which the methods, systems, circuitry, articles ofmanufacture, and computer program products and architecture, inaccordance with various embodiments, may be implemented by one or moreadvertisement server devices 237. As shown in FIG. 2A, one or morecomputing devices 220 may capture images. For example, computing device220 may capture an image of an entity 105 associated with a privacybeacon, e.g., a DCM (“Don't Capture Me”) beacon 110. In this and someother examples, the captured entity is named “Jules Caesar.”

Referring again to FIG. 2A, computing device 220 may capture the imagedata as image data 22, which may be optical data, e.g., light data,digital data, e.g., a digital signal, or data in another form. In aprocess that will be discussed in more detail herein according tovarious embodiments, image data 22 may be encrypted using adevice-specific code, shown here as encrypted image data 24. Encryptedimage data 24 may be transmitted to a server device 230, which may be anexample of wearable computer server 3000 shown in FIG. 1. In anembodiment, computing device 220 may generate beacon metadata 114 fromthe detected DCM beacon 110. In an embodiment, beacon metadata 114 maybe binary beacon metadata that indicates whether a beacon has beendetected, e.g., yes or no. In an embodiment, beacon metadata 114 mayinclude a data string that identifies the beacon, the entity, the typeof beacon, data about the beacon, or a combination of the foregoing. Inan embodiment, such a beacon metadata 114 may be used by server device230 to obtain additional information about the entity, e.g., terms ofservice data, which will be described in more detail herein. In anembodiment, beacon metadata 114 may include terms of service dataassociated with the entity, e.g., Jules Caesar. The types of beaconmetadata 114 are not limited to those listed in this paragraph, and theforegoing types of beacon metadata 114 will be described in more detailfurther herein with respect to FIGS. 4A-4E, and with respect to thespecific examples listed herein.

Referring again to FIG. 2A, in an embodiment, server device 230 mayinclude an encrypted image data block acquisition module (not pictured)that receives encrypted image data 24 from the computing device 220. Inan embodiment, server device 230 may include a beacon metadata handlingmodule (not pictured) that receives beacon metadata 114. In anembodiment, beacon metadata handling module may receive the beaconmetadata 114 and determine what, if any, actions should be taken toobtain more information regarding the entity 105 and/or the DCM beacon110.

Referring again to FIG. 2A, in an embodiment, valuation assessmentmodule 236 of server device 230 may determine a potential value of thecaptured image data 22, e.g., through advertisements, e.g.,context-sensitive advertisements, or other advertisements, that may beshown and viewers drawn to the advertisements through use of the imagedata 22. In an embodiment, the image data may be decrypted and may betransmitted back to computing device 220, where, in an embodiment, itmay then be accessed by other modules of the device, e.g., imageprocessing module 205, and/or a user 115 of the computing device 220.

In an embodiment, valuation assessment module 236 may determine not todecrypt and distribute the image, or valuation assessment module maydetermine to distribute the image, but also send the image for furtherprocessing. In an embodiment, server device 230 may then transmit theimage to advertisement server device 237. In an embodiment,advertisement server device 237 may be a part of server device 230. Inanother embodiment, advertisement server device 237 may be a separateserver, and may be owned by a separate entity. For example, in anembodiment, advertisement server device 237 may be operated by an entitythat is willing to take more risk for potential liability of using thecaptured image than an entity that controls server 230. In anembodiment, as will be described herein, advertisement server device 237may add advertisements, which may or may not obscure an entity thatappears in the picture, in order to change a potential liability forusing the image and/or to change a potential revenue collectable fromusing the image. As will be shown in FIG. 2B, advertisement serverdevice 237 is shown separately from server 230, for ease ofillustration.

Referring again to FIG. 2A, for example, in an embodiment, advertisementserver device 237 may include a valuation assessment module 242.Valuation assessment module 242 of advertisement server device 237 maybe similar to valuation assessment module 236 of server device 230, butmay have a different valuation calculation and/or estimation procedurein embodiments in which advertisement server device 237 is differentthan server device 230 or controlled by a different entity than serverdevice 230. In another embodiment, valuation assessment module 242 mayperform the same valuation as valuation assessment module 236, e.g., forcheck or verification purposes. In an embodiment, when advertisementserver device 237 and server device 230 are the same, valuationassessment module 242 may be omitted. In another embodiment, however, inwhich advertisement server device 237 and server device 230 are thesame, valuation assessment module 242 also may be present, and mayperform a same valuation to check the valuation of valuation assessmentmodule 236.

Referring again to FIG. 2A, in an embodiment, advertisement serverdevice 237 may include advertisement addition module 244. In anembodiment, advertisement addition module 244 may add a newadvertisement to image 24, which may already have one or moreadvertisements added to it, or which may be the same as the imagecaptured by the image capture device. In an embodiment, advertisementaddition module 244 may move an existing advertisement in image 24 to adifferent location, e.g., to cover more or less of the entity for whichthe DCM beacon 110 is associated. In an embodiment, the newadvertisement may be obtained from advertisement image obtaining module249 from a source of advertisements, e.g., either external or internal.In an embodiment, the advertisement image may be selected at leastpartly based on one or more features of the image 24.

Referring again to FIG. 2A, in an embodiment, after the advertisementimage is added to the image 24, updated valuation assessment module 246may perform a valuation of one or more of a potential revenue and apotential liability, in a process that will be described in more detailwith respect to the following figures, and the foregoing applicationswhich were incorporated by reference.

Referring again to FIG. 2A, in some embodiments, one or more of theencrypted image data and the DCM beacon metadata are transmitted overone or more communication network(s) 240. In various embodiments, thecommunication network 240 may include one or more of a local areanetwork (LAN), a wide area network (WAN), a metropolitan area network(MAN), a wireless local area network (WLAN), a personal area network(PAN), a Worldwide Interoperability for Microwave Access (WiMAX), publicswitched telephone network (PTSN), a general packet radio service (GPRS)network, a cellular network, and so forth. The communication networks240 may be wired, wireless, or a combination of wired and wirelessnetworks. It is noted that “communication network” as it is used in thisapplication refers to one or more communication networks, which may ormay not interact with each other.

Referring again to FIG. 2A, It is noted that, in an embodiment, one ormore of valuation assessment module 242, advertisement addition module244, advertisement image obtaining module 249, and updated valuationassessment module 246 may be part of processor 222 shown in FIG. 2B, ormay be combined, separated, distributed, and/or omitted in othercombinations not specifically enumerated here.

Referring again to FIG. 2A, computing device 220 may be any electronicdevice, portable or not, that may be operated by or associated with oneor more users. Computing device 220 is shown as interacting with a user115. As set forth above, user 115 may be a person, or a group of people,or another entity that mimics the operations of a user. In anembodiment, user 115 may be a computer or a computer-controlled device.Computing device 220 may be, but is not limited to, a wearable computer.Computing device 220 may be any device that is equipped with an imagecapturing component, including, but not limited to, a cellular phone, anetwork phone, a smartphone, a tablet, a music player, a walkie-talkie,a radio, an augmented reality device (e.g., augmented reality glassesand/or headphones), wearable electronics, e.g., watches, belts,earphones, or “smart” clothing, earphones, headphones, audio/visualequipment, media player, television, projection screen, flat screen,monitor, clock, appliance (e.g., microwave, convection oven, stove,refrigerator, freezer), a navigation system (e.g., a Global PositioningSystem (“GPS”) system), a medical alert device, a remote control, aperipheral, an electronic safe, an electronic lock, an electronicsecurity system, a video camera, a personal video recorder, a personalaudio recorder, and the like.

Referring now to FIG. 2B, FIG. 2B shows a detailed description of anadvertisement server device 237 operating in environment 200, in anembodiment. It is noted that the components shown in FIG. 2B representmerely one embodiment of advertisement server device 237, and any or allcomponents other than processor 222 may be omitted, substituted, ormodified, in various embodiments. It is further noted that althoughadvertisement server device 237 is shown as separate from server device230, this is merely for clarity in describing the invention. In one ormore embodiments, advertisement server device 237 may be part of serverdevice 230, and redundant modules or components may be omitted.

Referring again to FIG. 2B, advertisement server device 237 may includea server device memory 245. In an embodiment, device memory 245 mayinclude memory, random access memory (“RAM”), read only memory (“ROM”),flash memory, hard drives, disk-based media, disc-based media, magneticstorage, optical storage, volatile memory, nonvolatile memory, and anycombination thereof. In an embodiment, device memory 245 may beseparated from the device, e.g., available on a different device on anetwork, or over the air. For example, in a networked system, there maybe many advertisement server devices 237 whose device memory 245 islocated at a central server that may be a few feet away or locatedacross an ocean. In an embodiment, advertisement server device 237 mayinclude a device memory 245. In an embodiment, memory 245 may compriseof one or more of one or more mass storage devices, read-only memory(ROM), programmable read-only memory (PROM), erasable programmableread-only memory (EPROM), cache memory such as random access memory(RAM), flash memory, synchronous random access memory (SRAM), dynamicrandom access memory (DRAM), and/or other types of memory devices. In anembodiment, memory 245 may be located at a single network site. In anembodiment, memory 245 may be located at multiple network sites,including sites that are distant from each other.

Referring again to FIG. 2B, in an embodiment, advertisement serverdevice 237 may include valuation assessment module 242, advertisementaddition module 244, advertisement image obtaining module 249, andupdated valuation assessment module 246, as previously described withrespect to FIG. 2A. In an embodiment, for example, advertisement serverdevice 237 may include an image encryption/decryption module 262 thathandles the device-based and client-based encryption and decryption ofthe images, as described in this and other applications. In anembodiment, advertisement server device 237 may include an imagetransmitting module 266 for transmitting the image to a destinationafter addition of the advertisement image or images and the updatedvaluation.

Referring again to FIG. 2B, FIG. 2B shows a more detailed description ofadvertisement server device 237. In an embodiment, advertisement serverdevice 237 may include a processor 222. Processor 222 may include one ormore microprocessors, Central Processing Units (“CPU”), a GraphicsProcessing Units (“GPU”), Physics Processing Units, Digital SignalProcessors, Network Processors, Floating Point Processors, and the like.In an embodiment, processor 222 may be a server. In an embodiment,processor 222 may be a distributed-core processor. Although processor222 is as a single processor that is part of a single advertisementserver device 237, processor 222 may be multiple processors distributedover one or many advertisement server devices 237, which may or may notbe configured to operate together.

Processor 222 is illustrated as being configured to execute computerreadable instructions in order to execute one or more operationsdescribed above, and as illustrated in FIGS. 11, 12A-12C, 13A-13G,14A-14D, and 15A-15B. In an embodiment, processor 222 is designed to beconfigured to operate as processing module 250, which may include one ormore of captured image that depicts at least one entity receiving module252, estimated potential revenue generated from captured imagedistribution and estimated potential liability created from capturedimage distribution comparing module 254, at least one advertisementimage inserting into the captured image at a particular location module256, and updated estimated potential revenue from the captured imagewith the inserted at least one advertisement image at the particularlocation calculating module 258.

FIGS. 3A-3E refer to an “image capture device,” which is defined as anydevice that is equipped with the ability to capture images, and notnecessarily a wearable computer or a device designed specifically tocapture images.

Referring now to FIG. 3A, FIG. 3A shows an exemplary embodiment of acomputing device 220 as image capture device 302. In an embodiment,image capture device 302 may include an image capture component, e.g., alens 306A. Image capture component 306A may capture an image includingthe user 105 and the DCM beacon 110, and capture that image as raw(optical or digital) data 120. In an embodiment, image capture device302 may include beacon detection module 310A that is configured todetect DCM beacon 110, either optically, digitally, or other, dependingon the embodiment. After detection of the beacon, the image data may besent to an image data encryption module 320A to encrypt the image. In anembodiment, if the beacon is not detected, the image data 120 isreleased past barrier 340A and the other image capture device modules350A may operate on the image data 120. In an embodiment, the encrypteddata, and data associated with the DCM beacon 110 (although notnecessarily the beacon itself) may be transmitted to encrypted data andbeacon transmitting module 330A, which may transmit the encrypted dataand beacon data to an external source, e.g., server 3000 as described inFIG. 1. It is noted that beacon detection module 310A, image dataencryption module 320A, and encrypted data and beacon transmittingmodule 330A may be separated from other image capture device modules350A by barrier 340A.

In an embodiment, barrier 340A may be a physical barrier, e.g., beacondetection module 310A, lens 306A, image data encryption module 320A, andencrypted data and beacon transmitting module 330A may be hard-wired toeach other and electrically excluded from other image capture devicemodules 350A. In another embodiment, barrier 340A may be implemented asa programmed barrier, e.g., the image data 120 is not transmitted tomodules other than beacon detection module 310A, lens 306A, image dataencryption module 320A, and encrypted data and beacon transmittingmodule 330A. In another embodiment, barrier 340A may be implemented as adata access barrier, e.g., the captured image data 120 may be protected,e.g., with an access or clearance level, so that only beacon detectionmodule 310A, lens 306A, image data encryption module 320A, and encrypteddata and beacon transmitting module 330A may read or operate on theimage data 120. In another embodiment, barrier 340A may not be acomplete barrier, e.g., barrier 340A may allow “read” access to theimage data, but not “copy” or “write” access. In another embodiment,barrier 340A may be a barrier to transmission, e.g., the image may beviewed locally at the device, but may be barred from being saved to aremovable memory, or uploaded to a cloud storage or social networkingsite/social media site.

Referring now to FIG. 3B, FIG. 3B shows an embodiment of a computingdevice 220 as image capture device 304. In an embodiment, image capturedevice 304 may include an image capture component, e.g., a lens andsensor 306B. Image capture component 306B may capture an image includingthe user 105 and the DCM beacon 110, and capture that image as raw(optical or digital) data 120. In an embodiment, image capture device304 may include image path splitting module 305B that may receive theraw data 120 as a signal, e.g., optical or digital, and split the signalinto two branches. As shown in FIG. 3B, one branch, e.g., the northbranch, sends the raw signal to image data encryption module 320B, whichmay encrypt the image. In an embodiment, the other branch, e.g., thesouth branch, may send the signal to a beacon detection module 310B,which may detect the DCM beacon 110. In an embodiment, if the DCM beacon110 is detected, then the unencrypted image data that arrived at beacondetection module 310B is destroyed. In an embodiment, if the DCM beacon110 is not detected, then the encrypted image data from image dataencryption module 320B is destroyed, and the unencrypted image data atbeacon detection module 310B is allowed to pass to other image capturedevice modules 350B. In an embodiment, the beacon detection result andthe encrypted image data are transmitted to the encrypted data andbeacon transmitting module 330B. In an embodiment, barrier 340B mayseparate image path splitting module 305B, beacon detection module 310B,image data encryption module 320B, and encrypted data and beacontransmitting module 330B from other image capture device modules 350B.

In an embodiment, barrier 340B may be a physical barrier, e.g., beacondetection module 310B, lens 306B, image data encryption module 320B, andencrypted data and beacon transmitting module 330B may be hard-wired toeach other and electrically excluded from other image capture devicemodules 350B. In another embodiment, barrier 340B may be implemented asa programmed barrier, e.g., the image data 120 is not transmitted tomodules other than image path splitting module 305B, beacon detectionmodule 310B, lens 306B, image data encryption module 320B, and encrypteddata and beacon transmitting module 330B. In another embodiment, barrier340B may be implemented as a data access barrier, e.g., the capturedimage data may be protected, e.g., with an access or clearance level, sothat only beacon detection module 310B, lens 306B, image data encryptionmodule 320B, and encrypted data and beacon transmitting module 330B mayread or operate on the image data 120. In another embodiment, barrier340B may not be a complete barrier, e.g., barrier 340B may allow “read”access to the image data, but not “copy” or “write” access. In anotherembodiment, barrier 340B may be a barrier to transmission, e.g., theimage may be viewed locally at the device, but may be barred from beingsaved to a removable memory, or uploaded to a cloud storage or socialnetworking site/social media site.

Referring now to FIG. 3C, FIG. 3C shows an embodiment of a computingdevice 220 implemented as image capture device 306. In an embodiment,image capture device 306 may include an image capture component 306Cthat captures optical data 120A. In an embodiment, optical data 120A maybe sent to optical splitting module 305C, which may split the opticalsignal, e.g., the light, into two paths. Referring to FIG. 3C, the“south” path may transmit the light to an optical filter 312, which mayfilter the light for a specific characteristic, e.g., a wavelength or anobject, according to known optical filtration techniques. In anembodiment, the filtered optical signal may then be transmitted to afiltered optical signal beacon detection module 310C, which may detectthe beacon 110 in the optical data 120A.

Referring again to FIG. 3C, the “north” path from optical splittingmodule 305C may transmit the optical image data to an optical-to-digitalconverter 314, e.g., a CMOS or CCD sensor. In an embodiment, the digitalsignal then may be transmitted to image data encryption module 320C, andthe encrypted data transmitted to encrypted data and beacon transmittingmodule 330C, along with the beacon detection result, for transmission toan external source, e.g., server 3000 as shown in FIG. 1. In anembodiment, barrier 340C may prevent access to the unencrypted imagedata by other image capture device modules 350C. In an embodiment,barrier 340C may function similarly to barriers 340A and 340B, and thedescriptions of those barriers and their possible implementations alsomay apply to barrier 340C. In an embodiment, image data encryptionmodule 320C, encrypted data beacon and transmitting module 330C, andoptical-to-digital converter 314 may be controlled by beacon detectioncontrol module 325, which may be part of the processor of image capturedevice 306, or may be a separate processor. In an embodiment, beacondetection control module 325 may form part or all of processor 222 ofcomputing device 220 of FIG. 2B.

Referring now to FIG. 3D, FIG. 3D shows an exemplary implementation of acomputing device 220 implemented as image capture device 308, accordingto an embodiment. Image capture device 308 may include an optical imagecollector 306D that may capture an image including the user 105 and theDCM beacon 110, and capture that image as optical data 120A. Opticaldata 120A may then be sent to optical splitting module 305D, which maysplit the optical signal, e.g., the light, into two paths. Referring toFIG. 3D, the “south” path may transmit the light to an opticaltransformation module 332, which may apply a transformation, e.g., aFourier transformation, to the optical image data. The transformedoptical data from module 332, as well as a reference image from opticalbeacon reference signal providing module 334 may be transmitted tooptical beacon detection module 310D. Optical beacon detection module310D may optically detect the beacon using Fourier transformation and anoptical correlator. The basic operation of performing optical imageobject detection is described in the publically-available (at theUniversity of Michigan Online Library) paper “Report of ProjectMICHIGAN, SIGNAL DETECTION BY COMPLEX SPATIAL FILTERING,” by A. B.Vander Lugt, printed in July 1963 at the Radar Laboratory at theInstitute of Science and Technology, the University of Michigan, whichis hereby incorporated by reference in its entirety. Applicant'srepresentative is including a copy of this paper with the filing of thisapplication, for the convenience of the Examiner.

Referring again to FIG. 3D, the “north” path from optical splittingmodule 305D may transmit the optical image data to an optical-to-digitalconverter 324, e.g., a CMOS or CCD sensor. In an embodiment, the digitalsignal then may be transmitted to image data encryption module 320D, andthe encrypted data transmitted to encrypted data and beacon transmittingmodule 330D, along with the beacon detection result, for transmission toan external source, e.g., server 3000 as shown in FIG. 1. In anembodiment, barrier 340D may prevent access to the unencrypted imagedata by other image capture device modules 350D. In an embodiment,barrier 340D may function similarly to barriers 340A and 340B, and thedescriptions of those barriers and their possible implementations alsomay apply to barrier 340D. In an embodiment, image data encryptionmodule 320D, encrypted data and beacon transmitting module 330D, andoptical-to-digital converter 324 may be controlled by beacon detectioncontrol module 335, which may be part of the processor of image capturedevice 308, or may be a separate processor. In an embodiment, beacondetection control module 335 may form part or all of processor 222 ofcomputing device 220 of FIG. 2B.

Referring now to FIG. 3E, FIG. 3E shows an exemplary embodiment of animplementation of computing device 220 as image capture device 309. Inan embodiment, image capture device 309 may include an optical imagecollector 306E, e.g., a lens, which may collect the optical data 120A.Optical data 120A may be emitted to an optical beacon detection module310E, which may detect the DCM beacon 110 using one of theabove-described optical detection methods. After detection of the beaconusing optical techniques, the optical signal may be captured by anoptical-to-digital conversion module 344, and converted to digital imagedata, which is transferred to image data encryption module 320E forencryption. In an embodiment, modules 306E, 310E, 344, and 320E, arehard-wired to each other, and separated from encrypted data and beacontransmitting module 330E and other image capture device modules 350E bybarrier 340E (which, in this embodiment, is shown for exemplary purposesonly, because the physical construction of modules 306E, 310E, 344, and320E removes the need for a barrier 340E, whether implemented ashardware, programming, security, or access. In this embodiment, theimage data is encrypted prior to interaction with the “main” portions ofimage capture device 309, and after the beacon data has been opticallydetected.

FIGS. 4A-4E show one or more embodiments of a server device 230,according to one or more embodiments. Unless otherwise stated orcontradictory to FIGS. 4A-4E, the server devices 430A, 430B, 430C, 430D,and 430E may include the elements of server device 230, as previouslydescribed. Similarly, unless otherwise stated or contradictory to FIGS.4A-4E, the computing devices 420A, 420B, 420C, 420D, and 420E mayinclude the elements of computing device 220, as previously described.

Referring now to FIG. 4A, FIG. 4A shows an exemplary implementation ofserver device 230 as server device 430A operating in exemplaryenvironment 400A. In an embodiment, computing device 420A furtherincludes a location and time log and transmission module 422. In anembodiment, location and time log and transmission module 422 may recorda location, e.g., through global positioning sensors, triangulationusing radio signals, or other methods, of the computing device 420A, anda time that the image is captured, at the time the image is captured.This data of location and time of the image capture, e.g., location andtime of detection data 162, may be transmitted to server device 430A, asshown in FIG. 4A.

Referring again to FIG. 4A, server device 430A may include a beaconmetadata acquisition module 433. Beacon metadata acquisition module 433may include location and time of beacon detection data acquisitionmodule 433A. Location and time of beacon detection data acquisitionmodule 433A may receive the location and time of detection data 162. Inan embodiment in which the beacon metadata 150 is binary beacon metadata150A, additional data regarding the image may be obtained. For example,server device 430A may transmit the location and time of detection data162 to a remote location, e.g., to beacon support server 490. Beaconsupport server may include, for example, a geotagged and timestampedlist of detected beacons 436, which may track a location and time when abeacon is detected. Beacon support server 490 may be associated with DCMbeacon 110, and may be configured to log each time DCM beacon 110 isdetected, e.g., in an embodiment in which DCM beacon 110 is an activebeacon that can determine when it is detected. In an embodiment, beaconsupport server 490 may use the location and time of detection data 162to determine which DCM beacon 110 is detected, and transmit the beaconidentification information back to server device 430A, e.g., to beaconidentification data acquisition module 433B. In an embodiment, thisbeacon identification information may be used by server device 430A. Inan embodiment, the beacon identification information may be used toidentify the entity in the image, without decrypting the image, forexample.

Referring now to FIG. 4B, FIG. 4B shows an exemplary implementation ofserver device 230 as server device 430B operating in exemplaryenvironment 400B. In an embodiment, the computing device 420B maygenerate beacon metadata 150, which may be binary beacon metadata 150A,and transmit the binary beacon metadata 150A to server device 430B. Inan embodiment, server device 430B receives the binary beacon metadata150A, e.g., through use of beacon metadata acquisition module 443, whichmay describe whether a beacon was detected in the encrypted image datablock 160, but which may, in an embodiment, not provide additional dataregarding the beacon. In an embodiment, server device 430B may includeencrypted image analysis and data extraction module 442, which mayperform analysis on the encrypted image 24, if possible. Such analysismay include, for example, that the encrypted image data block 160 mayhave metadata that is not encrypted or that may be read through theencryption. In an embodiment, for example, the image 22 may be encryptedin such a manner that certain characteristics of the encrypted image 24may be obtained without decrypting the image. In an embodiment, serverdevice 430B may use encrypted image analysis and data extraction module442 to determine more information about the image, e.g., which may beused to perform valuation of the image and/or to retrieve term dataregarding one or more terms of service associated with the DCM beacon110 and the entity Jules Caesar 105.

Referring now to FIG. 4C, FIG. 4C shows an exemplary implementation ofserver device 230 as server device 430C operating in exemplaryenvironment 400C. In an embodiment, computing device 420C may transmitthe beacon metadata 150, which may be binary beacon metadata 150A, toserver device 430C. Beacon metadata 150 may be obtained by beaconmetadata acquisition module 456. In an embodiment, beacon metadataacquisition module 456 may relay data regarding the received metadata toa decision-making portion of server device 430C, e.g., a centralprocessor. In an embodiment, server device 430C may determine that itwants more data regarding the image 22, in order to retrieve term data,or perform a valuation of the image data. Accordingly, in an embodiment,server device 430C may include encrypted image analysis and dataextraction module 436, which may operate similarly to encrypted imageanalysis and data extraction module 442, and also, in an embodiment,encrypted image analysis and data extraction module 436 may transmit theencrypted image data block to a “sandbox,” e.g., image decryptionsandbox 492. Image decryption sandbox 492 may place the image in avirtual or physical “sandbox” where other processes may be unable toaccess the data. Image decryption sandbox 492 may be part of serverdevice 430C, or may be a separate entity. In an embodiment, imagedecryption sandbox 492 may decrypt the encrypted image. Encrypted imagedecryption and beacon identification module 493 may perform analysis onthe decrypted image, including identifying the beacon, or identifyingthe entity, or a combination thereof. The identification data then maybe given to beacon identification data reception module 438. In anembodiment, the decrypted image data is then trapped in the sandboxand/or destroyed.

Referring now to FIG. 4D, FIG. 4D shows an exemplary implementation ofserver device 230 as server device 430D operating in exemplaryenvironment 400D. In an embodiment, computing device 420D may transmitbeacon metadata 150, e.g., beacon identifier metadata 150B, to serverdevice 430D. In an embodiment, beacon identifier metadata 150B mayidentify the beacon, e.g., the DCM beacon 110. The identification may bea unique identification, e.g. “this beacon is associated with user#13606116, Jules Caesar,” or, in an embodiment, the identification maybe a class of beacon, e.g., “this is a beacon with a $100,000 dollarliquidated damages clause associated with using a likeness of the entityassociated with the beacon,” or “this is a beacon of a televisioncelebrity,” or “this is a beacon provided by Image Protect Corporation.”

Referring again to FIG. 4D, server device 430D receives the beaconidentifier metadata 150B, e.g., through use of beacon metadataacquisition module 447. In an embodiment, server device 430D maytransmit the identifier to an external location, e.g., a terms ofservice transmission server 485. Terms of service transmission server485 may store terms of service associated with various beacons in itsterms of service repository 489. In an embodiment, each unique beaconmay be associated with its own unique terms of service. In anotherembodiment, there may be common terms of service for various users. Inanother embodiment, there may be common terms of service for variousclasses of users. In an embodiment, the terms of service may varydepending on how much the entity, e.g., Jules Caesar, is paying to usethe beacon service.

In an embodiment, terms of service transmission server 485 may includebeacon identifier lookup table 487. Beacon identifier lookup table 487may receive the beacon identifier metadata 150B, and use the beaconidentifier metadata 150B to obtain the terms of service associated withthat beacon, e.g., terms of service data 151. In an embodiment, terms ofservice data 151 then may be transmitted to server device 430D.

Referring now to FIG. 4E, FIG. 4E shows an exemplary implementation ofserver device 230 as server device 430E operating in exemplaryenvironment 400E. In an embodiment, computing device 420E may detect theDCM beacon 110, and may obtain the terms of service from the detectedbeacon (e.g., the terms of service may be read from the beacon, e.g., incompressed binary). In an embodiment, the computing device 420E may usethe detected beacon data to obtain the terms of service data fromanother location, e.g., a terms of service data server (not pictured).

Referring again to FIG. 4E, in an embodiment, computing device 420E maytransmit beacon metadata 150, e.g., beacon identifier and terms ofservice metadata 150C, to server device 430E. Beacon metadataacquisition module 444 may receive the beacon identifier and terms ofservice metadata 150C, and detect that the terms of service are presentin the beacon metadata 150. In an embodiment, beacon metadata terms ofservice reading module 454 may read the terms of service from the beaconmetadata 150.

The foregoing examples are merely provided as examples of how beacondata may operate, and how identifying data and/or term of service datamay be obtained by the various server devices, and should not beinterpreted as limiting the scope of the invention, which is definedsolely by the claims. Any and all components of FIGS. 4A-4E may becombined with each other, modified, or eliminated.

FIGS. 5A-5D show one or more embodiments of a computing device 230,among other components, operating in an environment 500 (e.g.,500A-500D), according to one or more embodiments. Unless otherwisestated or contradictory to FIGS. 5A-5D, the server devices 530A, 530B,530C, and 530D may include the elements of server device 230, aspreviously described. Similarly, unless otherwise stated orcontradictory to FIGS. 5A-5D, the computing devices 520A, 520B, 520C,and 520D may include the elements of computing device 220, as previouslydescribed.

Referring now to FIG. 5A, FIG. 5A shows an exemplary implementation ofserver device 230 as server device 530A operating in exemplaryenvironment 500A. In an embodiment, as shown in FIG. 5A, computingdevice 520A may capture an image that includes an entity 105 that may beassociated with a privacy beacon, e.g., DCM beacon 110. In anembodiment, the captured image, e.g., image 22, may be encrypted intoencrypted image 24 using a device-based encryption key. In anembodiment, encrypted image 24 may be combined with beacon metadata,e.g., beacon metadata 150, in an encrypted image data block 160. Inanother embodiment, beacon metadata 150 may be separate from encryptedimage data block 160. In an embodiment, the encrypted image 24 may betransmitted to a server device 530A by encrypted image data transmittingmodule 180. In an embodiment, a decryption determination module 532A maydetermine to decrypt the image, e.g., in a process described in one ormore of this and/or previous applications incorporated by reference. Inan embodiment, server device 530A may include decryption module 534A,which may apply a device-based decryption key to the encrypted image 24to generate decrypted image data. In an embodiment, client-basedencryption module 536A may apply a client-based encryption key to thedecrypted image data, to generate a client-based encrypted image. In anembodiment, the client-based encrypted image then may be transmittedback to the computing device 520A, which may be a wearable computer,e.g., to client-based encrypted data receiving module 190. In anembodiment, upon receipt of the client-based encrypted image theclient-based encrypted image decryption module 195 may decrypt theclient-based encrypted image.

In an embodiment, one or more of the originally-captured image 22, thedecrypted image data in the decryption module 534A of server device530A, and the decrypted image data in the client-based encryption module536A may be identical. In another embodiment, the substantive portion ofthe data (e.g., the color data) may be identical, and other data, e.g.,header data or compression data, may be different. In anotherembodiment, the decrypted image data in the decryption module 534A ofserver device 530A, and the decrypted image data in the client-basedencryption module 536A may be slightly different.

Referring now to FIG. 5B, FIG. 5B shows an exemplary implementation ofserver device 230 as server device 530B operating in exemplaryenvironment 500B. In an embodiment, as shown in FIG. 5B, computingdevice 520B may capture an image that includes an entity 105 that may beassociated with a privacy beacon, e.g., DCM beacon 110. In anembodiment, the captured image, e.g., image 22, may be encrypted intoencrypted image 24 using a device-based encryption key. In anembodiment, encrypted image 24 may be combined with beacon metadata,e.g., beacon metadata 150, in an encrypted image data block 160. Inanother embodiment, beacon metadata 150 may be separate from encryptedimage data block 160. In an embodiment, the encrypted image 24 may betransmitted to a server device 530B by encrypted image data transmittingmodule 180. In an embodiment, a decryption determination module 532B maydetermine to decrypt the image, e.g., in a process described in one ormore of this and/or previous applications incorporated by reference. Inan embodiment, server device 530B may include decryption module 534B,which may apply a device-based decryption key to the encrypted image 24to generate decrypted image data. In an embodiment, client-basedencryption module 536B may apply a client-based encryption key to thedecrypted image data, to generate a client-based encrypted image.

Referring again to FIG. 5B, in an embodiment, the client-based encryptedimage then may be transmitted to a device that is not the computingdevice 520B, e.g., rather to other client-associated computer device550. Other client-associated computer device 550 may includeclient-based encrypted data receiving module 191 and/or client-basedencrypted image decryption module 194 which may decrypt the client-basedencrypted image, similarly to modules 190 and 195 of FIG. 5A, but notpart of computing device 520B. In an embodiment, computer device 550 mayalso be worn or carried by the client, e.g., a smartphone carried by theclient that was wearing the wearable computer 520B. In an embodiment,computer device 550 may be remote from the client, e.g., the client'shome computer. In another embodiment, computer device 550 may be ashared server, e.g., where the client stores images on the cloud. In anembodiment similar to the one described above, the computing device 520Bmay not possess the decrypted image at any point during the process.

Referring again to FIG. 5B, similarly to FIG. 5A, in an embodiment, oneor more of the originally-captured image 22, the decrypted image data inthe decryption module 534B of server device 530B, and the decryptedimage data in the client-based encryption module 536B may be identical.In another embodiment, the substantive portion of the data (e.g., thecolor data) may be identical, and other data, e.g., header data orcompression data, may be different. In another embodiment, the decryptedimage data in the decryption module 534B of server device 530B, and thedecrypted image data in the client-based encryption module 536B may beslightly different.

Referring now to FIG. 5C, FIG. 5C shows an exemplary implementation ofserver device 230 as server device 530C operating in exemplaryenvironment 500C. In an embodiment, as shown in FIG. 5C, computingdevice 520C may capture an image that includes an entity 105 that may beassociated with a privacy beacon, e.g., DCM beacon 110. In anembodiment, the captured image, e.g., image 22, may be encrypted intoencrypted image 24 using a device-based encryption key. In anembodiment, encrypted image 24 may be combined with beacon metadata,e.g., beacon metadata 150, in an encrypted image data block 160. Inanother embodiment, beacon metadata 150 may be separate from encryptedimage data block 160. In an embodiment, the encrypted image 24 may betransmitted to a server device 530C by encrypted image data transmittingmodule 180. In an embodiment, a decryption determination module 532C maydetermine to decrypt the image, e.g., in a process described in one ormore of this and/or previous applications incorporated by reference.

Referring again to FIG. 5C, in an embodiment, one or more of thedecision to decrypt the encrypted image 24, and the encrypted image 24may be transmitted to a client-based encryption handling device 560. Inan embodiment, client-based encryption handling device 560 may includedecryption module 562, which may apply a device-based decryption key tothe encrypted image 24 to generate decrypted image data. In anembodiment, client-based encryption module 564 may apply a client-basedencryption key to the decrypted image data, to generate a client-basedencrypted image. In an embodiment, the client-based encrypted image,then may be transmitted back to the computing device 520A, which may bea wearable computer, e.g., to client-based encrypted data receivingmodule 190. In an embodiment, upon receipt of the client-based encryptedimage the client-based encrypted image decryption module 195 may decryptthe client-based encrypted image.

Referring again to FIG. 5C, similarly to FIG. 5A, in an embodiment, oneor more of the originally-captured image 22, the decrypted image data inthe decryption module 562 of client-based encryption handling device560, and the decrypted image data in the client-based encryption module564 may be identical. In another embodiment, the substantive portion ofthe data (e.g., the color data) may be identical, and other data, e.g.,header data or compression data, may be different. In anotherembodiment, the decrypted image data in the decryption module 562 ofclient-based encryption handling device 560, and the decrypted imagedata in the client-based encryption module 564 may be slightlydifferent.

Referring now to FIG. 5D, FIG. 5D shows an exemplary implementation ofserver device 230 as server device 530D operating in exemplaryenvironment 500D. In an embodiment, as shown in FIG. 5D, computingdevice 520D may capture an image that includes an entity 105 that may beassociated with a privacy beacon, e.g., DCM beacon 110. In anembodiment, the captured image, e.g., image 22, may be encrypted intoencrypted image 24 using a device-based encryption key. In anembodiment, encrypted image 24 may be combined with beacon metadata,e.g., beacon metadata 150, in an encrypted image data block 160. Inanother embodiment, beacon metadata 150 may be separate from encryptedimage data block 160. In an embodiment, the encrypted image 24 may betransmitted to a server device 530D by encrypted image data transmittingmodule 180. In an embodiment, a decryption determination module 532D maydetermine to decrypt the image, e.g., in a process described in one ormore of this and/or previous applications incorporated by reference. Inan embodiment, server device 530D may include decryption module 534D,which may apply a device-based decryption key to the encrypted image 24to generate decrypted image data. In an embodiment, client-basedencryption module 536D may apply a client-based encryption key to thedecrypted image data, to generate a client-based encrypted image.

Referring again to FIG. 5D, in an embodiment, the client-based encryptedimage then may be transmitted to a device that is not the computingdevice 520D, e.g., rather to a social networking server 570 or filerepository 570. In an embodiment, social networking server 570 mayinclude client-based encrypted data receiving module 192, similarly toclient-based encrypted data receiving module 190 of FIG. 5A. In anembodiment, social networking server 570 may include the client-basedencrypted image decryption module 197, which may be similar to theclient-based encrypted image decryption module 195 of FIG. 5A, and whichmay decrypt the client-based encrypted image. In an embodiment, socialnetworking server may automatically decrypt the image, and/or take oneor more actions, e.g., posting the image to a user's account, e.g.,their “wall” on Facebook, or a similar structure. In another embodiment,the social networking server 570 may wait to decrypt the image, and/orto take one or more actions with the image, until the client thatcaptured the image logs into the social networking service associatedwith the social networking server.

Referring again to FIG. 5D, similarly to FIG. 5A, in an embodiment, oneor more of the originally-captured image 22, the decrypted image data inthe decryption module 534D of server device 530D, and the decryptedimage data in the client-based encryption module 536D may be identical.In another embodiment, the substantive portion of the data (e.g., thecolor data) may be identical, and other data, e.g., header data orcompression data, may be different. In another embodiment, the decryptedimage data in the decryption module 534D of server device 530D, and thedecrypted image data in the client-based encryption module 536D may beslightly different.

FIG. 6A shows an insertion of an advertisement into an image, accordingto one or more embodiments. Specifically, as shown in FIG. 6A, an image24 may have been taken by an image capture device, and may include anentity 105 associated with a DCM beacon (represented as an extended palmin FIG. 6A). When this image is valued, an estimated revenue generatedmay be represented as “X,” and an estimated liability incurred may berepresented as “L.” After addition of advertisement 106, the image maybe modified image 14A. In this embodiment, the ad may not cover theentity associated with the DCM beacon. In an embodiment, the newestimated revenue generated may be represented as X+X_(ad), whichrepresents one of numerous ways of calculating updated estimatedrevenue.

FIG. 6B shows an insertion of an advertisement into an image, accordingto one or more embodiments. Specifically, as shown in FIG. 6B, an image24 may have been taken by an image capture device, and may include anentity 105 associated with a DCM beacon (represented as an extended palmin FIG. 6B). When this image is valued, an estimated revenue generatedmay be represented as “X,” and an estimated liability incurred may berepresented as “L.” After addition of advertisement 106, the image maybe modified image 14B. In this embodiment, the ad may cover the entityassociated with the DCM beacon. In an embodiment, the new estimatedrevenue generated may be represented as X+X_(ad), which represents oneof numerous ways of calculating updated estimated revenue. The newestimated liability incurred may be represented by L−L_(cover), since,in some versions of the calculations, the potential liability may belowered because the advertisement 106 covers some or all of the entity105 associated with the DCM beacon. Although FIG. 6B shows theadvertisement completely covering the face of entity 105, in anembodiment, the advertisement 106 may cover other parts of entity 105,or may cover portions of parts of entity 105.

FIG. 6C shows an insertion of an advertisement into an image, accordingto one or more embodiments. Specifically, as shown in FIG. 6C, an image24A may have been taken by an image capture device, and may include anentity 105 associated with a DCM beacon (represented as an extended palmin FIG. 6C), and may have an advertisement present that is in aninconspicuous place, e.g., may not cover the entity associated with theDCM beacon. When image 24A is valued, an estimated revenue generated maybe represented as “X_(ad),” and an estimated liability incurred may berepresented as “L.” In this embodiment, the advertisement 106 may notcover the entity 105, but after the modification, the advertisement 106may overlap the entity 105 associated with the DCM beacon. In anembodiment, the updated estimated revenue generated may beX_(ad)+X_(coverad) and the estimated liability incurred may berepresented by L−L_(cover), since, in some versions of the calculations,the potential liability may be lowered because the advertisement 106covers some or all of the entity 105 associated with the DCM beacon.

Referring now to FIG. 7, FIG. 7 illustrates an exemplary implementationof the captured image that depicts at least one entity receiving module252. As illustrated in FIG. 7, the captured image that depicts at leastone entity receiving module may include one or more sub-logic modules invarious alternative implementations and embodiments. For example, asshown in FIG. 7, e.g., FIG. 7A, in an embodiment, module 252 may includeone or more of captured image that depicts a feature of the at least oneentity receiving module 702 and captured image that depicts at least oneentity receiving from an image capture device module 710. In anembodiment, module 702 may include captured image that depicts a face ofa celebrity receiving module 704. In an embodiment, module 704 mayinclude captured image that depicts a face of a celebrity receiving froman image capture device that captured the image module 706. In anembodiment, module 706 may include captured image that depicts a face ofa celebrity receiving from a wearable computer that covertly capturedthe image module 708. In an embodiment, module 710 may include capturedimage that depicts at least one entity receiving from a wearablecomputer device that is configured to covertly capture one or moreimages module 712.

Referring again to FIG. 7, e.g., FIG. 7B, in an embodiment, module 252may include one or more of captured image that depicts at least oneentity receiving from a server module 714 and captured image that haspreviously been encrypted and that depicts at least one entity receivingmodule 720. In an embodiment, module 714 may include captured image thatdepicts at least one entity receiving from a server that is configuredto perform one or more processes on one or more captured images capturedby an image capture device module 716. In an embodiment, module 716 mayinclude captured image that depicts at least one entity receiving from aserver that is configured to compare a potential revenue generated fromdistribution of the captured image to a potential liability incurredfrom the distribution of the captured image module 718. In anembodiment, module 720 may include one or more of captured image thathas previously been encrypted by an image capture device that capturedthe captured image that depicts at least one entity receiving module 722and captured image that has previously been encrypted through use of aunique device code associated with an image capture device that capturedthe captured image that depicts at least one entity receiving module726. In an embodiment, module 722 may include captured image that haspreviously been encrypted by an image capture device to hinderunauthorized distribution of the captured image that depicts at leastone entity receiving module 724.

Referring again to FIG. 7, e.g., FIG. 7C, in an embodiment, module 252may include captured image that depicts at least one entity and thatcontains a privacy beacon receiving module 728. In an embodiment, module728 may include one or more of captured image that depicts at least oneentity and that contains a privacy beacon that is associated with thedepicted at least one entity receiving module 730, captured image thatdepicts at least one entity and that contains a privacy beacon that isconfigured to facilitate acquisition of data that regards the depictedentity receiving module 732, and captured image that depicts at leastone entity and that contains a privacy beacon that was detected by animage capture device that captured the captured image receiving module736. In an embodiment, module 732 may include captured image thatdepicts at least one entity and that contains a privacy beacon that isconfigured to facilitate acquisition of term data that includes one ormore terms of service that govern distribution of the captured imagethat depicts the at least one entity receiving module 734.

Referring now to FIG. 8, FIG. 8 illustrates an exemplary implementationof estimated potential revenue generated from captured imagedistribution and estimated potential liability created from capturedimage distribution comparing module 254. As illustrated in FIG. 8, theestimated potential revenue generated from captured image distributionand estimated potential liability created from captured imagedistribution comparing module 254 may include one or more sub-logicmodules in various alternative implementations and embodiments. Forexample, as shown in FIG. 8, e.g., FIG. 8A, in an embodiment, module 254may include one or more of estimated potential revenue generated fromcaptured image release to a social networking website and estimatedpotential liability created from captured image distribution comparingmodule 802, estimated potential revenue generated from a direct sale ofthe captured image and the estimated potential liability created fromcaptured image distribution comparing module 804, amount of estimatedpotential revenue generated from captured image distribution to anamount of estimated potential liability created from captured imagedistribution ratio evaluation module 806, revenue value that correspondsto an estimated potential advertisement revenue generated from thedistribution of the captured image estimating module 808, estimateddamage value that corresponds to the estimated potential liabilityincurred for distribution of the captured image obtaining module 810,and comparison between the estimated revenue value and the obtainedestimated damage value executing module 812.

Referring again to FIG. 8, e.g., FIG. 8B, in an embodiment, module 254may include one or more of revenue value that corresponds to theestimated potential advertisement revenue generated from distribution ofthe captured image estimating module 814 and comparison between theestimated revenue value and the estimated potential liability createdfrom captured image distribution performing module 816. In anembodiment, module 814 may include one or more of revenue value thatcorresponds to the estimated potential advertisement revenue generatedfrom a post of the captured image to a social network website estimatingmodule 818, revenue value that corresponds to the estimated potentialadvertisement revenue generated from a post of the captured image to aphoto repository estimating module 820, and the revenue value thatcorresponds to the estimated potential advertisement revenue generatedfrom a post of the captured image to a themed web site estimating module822. In an embodiment, module 822 may include one or more of the revenuevalue that corresponds to the estimated potential advertisement revenuegenerated from one or more clicks on an advertisement link on the themedwebsite to which the captured image is posted estimating module 824, therevenue value that corresponds to the estimated potential advertisementrevenue generated from one or more product purchases after a view of anadvertisement on the themed website to which the captured image isposted estimating module 826, and the revenue value that corresponds tothe estimated potential advertisement revenue generated from one or moreclicks on an advertisement image inserted into the captured image postedto the themed web site estimating module 828.

Referring again to FIG. 8, e.g., FIG. 8C, in an embodiment, module 254may include module 814, module 816, and module 822, as previouslydescribed. In an embodiment, module 822 may include the revenue valuethat corresponds to the estimated potential advertisement revenuegenerated through increased traffic to the themed website to which thecaptured image is posted estimating module 830.

Referring again to FIG. 8, e.g., FIG. 8D, in an embodiment, module 254may include one or more of estimated damage value that corresponds tothe estimated potential liability created from captured imagedistribution obtaining module 832 and comparison between the obtainedestimated damage value and the estimated potential revenue generatedfrom captured image distribution performing module 834. In anembodiment, module 832 may include one or more of estimated damage valuethat corresponds to the estimated potential liability created fromcaptured image distribution at least partly based on term data thatincludes one or more terms of service that govern distribution of thecaptured image obtaining module 836 and estimated damage value thatcorresponds to the estimated potential liability created from capturedimage distribution that is at least partly based on privacy metadatarelated to the captured image obtaining module 840. In an embodiment,module 836 may include estimated damage value that corresponds to theestimated potential liability created from captured image distributionat least partly based on term data that includes one or more terms ofservice that specify a liquidated damages clause that applies todistribution of the captured image obtaining module 838. In anembodiment, module 840 may include estimated damage value thatcorresponds to the estimated potential liability created from capturedimage distribution that is at least partly based on privacy metadatathat identifies the depicted entity in the captured image obtainingmodule 842.

Referring again to FIG. 8, e.g., FIG. 8E, in an embodiment, module 254may include one or more of module 832 and module 834, as previouslydescribed. In an embodiment, module 832 may include one or more ofprivacy metadata related to the captured image acquiring module 844 andestimated damage value that corresponds to the estimated potentialliability created from captured image distribution at least partly basedon the acquired privacy metadata obtaining module 846. In an embodiment,module 844 may include one or more of privacy metadata related to aprivacy beacon linked to an entity depicted in the captured imageacquiring module 848 and privacy metadata related to a privacy beaconthat identifies an entity depicted in the captured image acquiringmodule 852. In an embodiment, module 848 may include privacy metadatarelated to the privacy beacon linked to the entity depicted in thecaptured image and that was detected in the captured image acquiringmodule 850.

Referring again to FIG. 8, e.g., FIG. 8F, in an embodiment, module 254may include one or more of module 832, module 834, module 844, andmodule 846, as previously described. In an embodiment, module 844 mayinclude privacy metadata configured to facilitate retrieval of term datathat regards the entity depicted in the captured image acquiring module854. In an embodiment, module 854 may include one or more of privacymetadata that is an identification code of a privacy beacon associatedwith the entity depicted in the captured image and that is configured tofacilitate retrieval of the term data acquiring module 856 and privacymetadata configured to facilitate retrieval of term data that includesone or more terms of service that regard distribution of captured imagethat depicts the entity acquiring module 860. In an embodiment, module856 may include privacy metadata that is an identification code of aprivacy beacon associated with the entity depicted in the captured imageand that is configured to facilitate retrieval of the term data from adatabase acquiring module 858.

Referring again to FIG. 8, e.g., FIG. 8G, in an embodiment, module 254may include one or more of potential advertisement revenue generatedfrom distribution of captured image that includes an advertisementplaced in the captured image distribution estimating module 862 andestimated potential advertisement revenue and estimated potentialliability created from captured image distribution comparing module 864.In an embodiment, module 862 may include potential advertisement revenuegenerated from distribution of captured image that includes anadvertisement placed in the captured image at a position away from thedepicted entity distribution estimating module 866.

Referring now to FIG. 9, FIG. 9 illustrates an exemplary implementationof at least one advertisement image inserting into the captured image ata particular location module 256. As illustrated in FIG. 9, the at leastone advertisement image inserting into the captured image at aparticular location module 256 may include one or more sub-logic modulesin various alternative implementations and embodiments. For example, asshown in FIG. 9, e.g., FIG. 9A, in an embodiment, module 256 may includeone or more of captured image modification through placement of the atleast one advertisement image into the captured image at the particularlocation facilitating module 902. In an embodiment, module 902 mayinclude one or more of captured image modification through placement ofthe at least one advertisement image into the captured image at theparticular location that is at least partially determined by one or moreof the estimated potential revenue and the estimated potential liabilityfacilitating module 904, captured image modification through placementof the at least one advertisement image into the captured image at theparticular location that has a distance from a center of the capturedimage that is proportional to a difference between the estimatedpotential revenue and the estimated potential liability facilitatingmodule 906, and captured image modification through placement of the atleast one advertisement image into the captured image at the particularlocation that overlaps the depicted entity when the estimated potentialrevenue is less than the estimated potential liability facilitatingmodule 910. In an embodiment, module 906 may include captured imagemodification through placement of the at least one advertisement imageinto the captured image at the particular location that has a distancefrom a center of the captured image that is decreased as a differencebetween the estimated potential revenue and the estimated potentialliability is decreased facilitating module 908. In an embodiment, module910 may include captured image modification through placement of the atleast one advertisement image into the captured image at the particularlocation that overlaps a percentage of the depicted entity that isproportional to an amount by which the estimated potential revenue isless than the estimated potential liability facilitating module 912.

Referring again to FIG. 9, e.g., FIG. 9B, in an embodiment, module 256may include one or more of at least one advertisement image insertinginto the captured image at a particular location that covers thedepicted entity in the captured image when the estimated potentialliability is greater than a particular amount module 914, at least oneadvertisement image inserting into the captured image at a particularlocation that covers the depicted entity in the captured image when theestimated potential revenue is less than a particular amount module 916,and captured image modification through placement of the at least oneadvertisement image into the captured image at the particular locationthat overlaps the depicted entity in the captured image facilitatingmodule 918. In an embodiment, module 918 may include one or more ofcaptured image modification through placement of the at least oneadvertisement image into the captured image at the particular locationthat obscures at least one feature of the depicted entity in thecaptured image facilitating module 920 and captured image modificationthrough placement of the at least one advertisement image into thecaptured image at the particular location that completely covers thedepicted entity in the captured image facilitating module 922.

Referring again to FIG. 9, e.g., FIG. 9C, in an embodiment, module 256may include captured image modification through placement of a removableadvertisement image into the captured image at the particular locationfacilitating module 924. In an embodiment, module 924 may include one ormore of captured image modification through placement of a removableadvertisement image into the captured image at the particular locationthat overlaps the depicted entity facilitating module 926, capturedimage modification through placement of a removable advertisement imageinto the captured image at the particular location that is configured tobe removed in response to a mouse click facilitating module 928, andcaptured image modification through placement of a removableadvertisement image into the captured image at the particular locationthat is configured to be removed in response to a mouse over theremovable advertisement image facilitating module 930.

Referring again to FIG. 9, e.g., FIG. 9D, in an embodiment, module 256may include one or more of new image that includes the captured imageand at least one advertisement image at the particular locationgenerating module 932, at least one advertisement image addition byoverlaying the at least one advertisement image into the captured imageat the particular location module 934, and at least one advertisementimage insertion into the captured image by overwriting the capturedimage at the particular location module 936.

Referring now to FIG. 10, FIG. 10 illustrates an exemplaryimplementation of updated estimated potential revenue from the capturedimage with the inserted at least one advertisement image at theparticular location calculating module 258. As illustrated in FIG. 10,the updated estimated potential revenue from the captured image with theinserted at least one advertisement image at the particular locationcalculating module 258 may include one or more sub-logic modules invarious alternative implementations and embodiments. For example, asshown in FIG. 10, e.g., FIG. 10A, in an embodiment, module 258 mayinclude one or more of updated estimated potential revenue from thecaptured image with the inserted at least one advertisement image at theparticular location projecting module 1002, updated estimated potentialrevenue from the captured image with the inserted at least oneadvertisement image at the particular location calculating by additionof an estimated revenue from the at least one advertisement image to atleast a portion of the estimated potential revenue module 1006, andupdated estimated potential revenue from the captured image with theinserted at least one advertisement image at the particular locationcalculating by multiplication of the estimated potential revenue by aparticular modifier module 1008. In an embodiment, module 1002 mayinclude updated estimated potential revenue from the captured image withthe inserted at least one advertisement image at the particular locationprojecting at least partly based on an estimated number of clicks on theat least one advertisement image module 1004. In an embodiment, module1008 may include one or more of updated estimated potential revenue fromthe captured image with the inserted at least one advertisement image atthe particular location calculating by multiplication of the estimatedpotential revenue by an entity identity classification modifier module1010 and updated estimated potential revenue from the captured imagewith the inserted at least one advertisement image at the particularlocation calculating by multiplication of the estimated potentialrevenue by an advertisement image classification modifier module 1012.

Referring again to FIG. 10, e.g., FIG. 10B, in an embodiment, module 258may include one or more of updated estimated potential revenue from thecaptured image with the inserted at least one advertisement image at theparticular location receiving from a remote location module 1014 andupdated estimated potential revenue from the captured image with theinserted at least one advertisement image at the particular locationcalculating at least partly based on term data associated with thecaptured entity module 1016.

Referring now to FIG. 11, FIG. 11 shows operation 1100, e.g., an exampleoperation of advertisement server device 237 operating in an environment200. In an embodiment, operation 1100 may include operation 1102depicting acquiring a captured image, wherein the captured image depictsat least one entity. For example, FIG. 2, e.g., FIG. 2B, shows capturedimage that depicts at least one entity receiving module 252 acquiring(e.g., obtaining, receiving, calculating, selecting from a list or otherdata structure, receiving, retrieving, or receiving informationregarding, performing calculations to find out, retrieving data thatindicates, receiving notification, receiving information that leads toan inference, whether by human or automated process, or being party toany action or transaction that results in informing, inferring, ordeducting, including but not limited to circumstances without absolutecertainty, including more-likely-than-not and/or other thresholds) acaptured image (e.g., a description of a graphic picture that is avisual representation of something, regardless of whether that somethingis coherent, nonsensical, abstract, or otherwise), wherein the capturedimage (e.g., the description of a graphic picture that is a visualrepresentation of something, regardless of whether that something iscoherent, nonsensical, abstract, or otherwise) depicts (e.g., shows,represents, suggests, causes someone to think of or conceptualize, is arepresentation of) at least one entity (e.g., a thing, e.g., a person, arock, a deer, a thought, a beam of light, e.g., anything that hasseparate and distinct existence or imagined existence and objective orconceptual reality).

Referring again to FIG. 11, operation 1100 may include operation 1104depicting performing a comparison between an estimated potential revenuegenerated from a distribution of the captured image and an estimatedpotential liability incurred for the distribution of the captured image.For example, FIG. 2, e.g., FIG. 2B, shows estimated potential revenuegenerated from captured image distribution and estimated potentialliability created from captured image distribution comparing module 254performing (e.g., carrying out, executing, taking one or more steps insupport of, taking one or more steps in furtherance of, facilitating,attempting, instructing to execute, assisting in execution, configuringa series of logical gates in a manner that allows performance of,delivering instructions to) a comparison (e.g., a relative weighing,which may be of numerical values or other representations, e.g.,conceptual sizes or abstract values) between an estimated (e.g., it isnot required that the number be exact or be based on events that havealready happened, e.g., it may be a projection, an estimation, a guess,an educated guess, it may or may not be based on recent events, it maybe part or all of a calculation, a result of a simulation, an inputtedestimate, a valuation, an assessment) potential (e.g., the revenue maynot have been realized yet, and may not be in real dollars) revenue(e.g., any form of measurable compensation, whether monetary or not, andincluding intangible value, e.g., goodwill, brand value, word-of-mouth,and the like, in addition to monetary value, whether electronic or real,and whether realized or not) generated (e.g., was at least partly aresult of, whether direct or indirect, whether intentional oraccidental) from a distribution (e.g., a release that facilitates one ormore people to view, e.g., copying to a drive, e-mailing, posting to aweb site, whether viewed or not, viewing on the device that captured theimage, transmitting to one or more devices, copying to a cloud location,making available to a web crawler, e.g., Google Image Search, makingavailable to a social network, e.g., Facebook or Twitter, posting on ablog) of the captured image (e.g., the description of a graphic picturethat is a visual representation of something, regardless of whether thatsomething is coherent, nonsensical, abstract, or otherwise), and anestimated (e.g., it is not required that the number be exact or be basedon events that have already happened, e.g., it may be a projection, anestimation, a guess, an educated guess, it may or may not be based onrecent events, it may be part or all of a calculation, a result of asimulation, an inputted estimate, a valuation, an assessment) potential(e.g., the revenue may not have been realized yet, and may not be inreal dollars) liability (e.g., legal or other damages that may berecovered by the entity in the captured image against any party to thedistribution of the image, including one or more componentmanufacturers, service providers, application developers, and the likethat may be involved in one or more hardware or software components thataided in the capture of the image, whether remote or local, andregardless of the size of their role, which damages may include directdamages paid to the aggrieved entity, or to another entity, and mayinclude other damages including the cost of defending claims or dealingwith complaints, whether officially or unofficially, and whether in alegal forum or other form) incurred (e.g., that came about as aproximate cause of) for the distribution (e.g., a release thatfacilitates one or more people to view, e.g., copying to a drive,e-mailing, posting to a web site, whether viewed or not, viewing on thedevice that captured the image, transmitting to one or more devices,copying to a cloud location, making available to a web crawler, e.g.,Google Image Search, making available to a social network, e.g.,Facebook or Twitter, posting on a blog) of the captured image (e.g., thedescription of a graphic picture that is a visual representation ofsomething, regardless of whether that something is coherent,nonsensical, abstract, or otherwise).

Referring again to FIG. 11, operation 1100 may include operation 1106depicting modifying the captured image by placing at least oneadvertisement image at a particular location of the captured image. Forexample, FIG. 2, e.g., FIG. 2B, shows at least one advertisement imageinserting into the captured image at a particular location module 256modifying (e.g., to make a change to, regardless of size or scope of thechange, and whether temporary or permanent, whether reversible orirreversible, and including preserving the original or not preservingthe original, adding to, subtracting from, making visible changes,making invisible changes, changing, mutating, adapting, modulating,adjusting, converting, encrypting, decrypting, encoding, sharpening,manipulating, compressing) the captured image (e.g., the description ofa graphic picture that is a visual representation of something,regardless of whether that something is coherent, nonsensical, abstract,or otherwise) by placing (e.g., overlaying, replacing, combining,blending, copying and making new, adding a layer to, modifying) at leastone advertisement image (e.g., any graphical representation of a productor service that is capable of being obtained by at least one entity) ata particular location (e.g., any part of, whether visible or invisible,e.g., in the corner, in the center, above, below, over all pixels of aparticular color value, over a face of a person, over a person's privateparts, over a corporate logo, etc.) of the captured image (e.g., thedescription of a graphic picture that is a visual representation ofsomething, regardless of whether that something is coherent,nonsensical, abstract, or otherwise).

Referring again to FIG. 11, operation 1100 may include operation 1108depicting calculating an updated estimated potential revenue from themodified captured image that includes the at least one advertisementimage at the particular location. For example, FIG. 2, e.g., FIG. 2B,shows updated estimated potential revenue from the captured image withthe inserted at least one advertisement image at the particular locationcalculating module 258 calculating (e.g., taking one or more steps inthe furtherance of finding a number, answer, estimate, valuation, etc.)an updated (e.g., something that has had information, data, analgorithm, function, operation, or the like applied to it, whetherchanged or unchanged, from a previous version) estimated (e.g., it isnot required that the number be exact or be based on events that havealready happened, e.g., it may be a projection, an estimation, a guess,an educated guess, it may or may not be based on recent events, it maybe part or all of a calculation, a result of a simulation, an inputtedestimate, a valuation, an assessment) potential (e.g., the revenue maynot have been realized yet, and may not be in real dollars) revenue(e.g., any form of measurable compensation, whether monetary or not, andincluding intangible value, e.g., goodwill, brand value, word-of-mouth,and the like, in addition to monetary value, whether electronic or real,and whether realized or not) from the modified (e.g., changed,including, but not limited to, make a change to, regardless of size orscope of the change, and whether temporary or permanent, whetherreversible or irreversible, and including preserving the original or notpreserving the original, adding to, subtracting from, making visiblechanges, making invisible changes, changing, mutating, adapting,modulating, adjusting, converting, encrypting, decrypting, encoding,sharpening, manipulating, compressing) captured image (e.g., thedescription of a graphic picture that is a visual representation ofsomething, regardless of whether that something is coherent,nonsensical, abstract, or otherwise) that includes the at least oneadvertisement image (e.g., any graphical representation of a product orservice that is capable of being obtained by at least one entity) at theparticular location (e.g., any part of, whether visible or invisible,e.g., in the corner, in the center, above, below, over all pixels of aparticular color value, over a face of a person, over a person's privateparts, over a corporate logo, etc.).

An example terms of service is listed below with the numbered paragraphs1-5. Many other variations of terms of service are known and used inclick-through agreements that are common at the time of filing, and theherein example is intended to be exemplary only and not limiting in anyway.

1. By capturing an image of any part of the user Jules Caesar(hereinafter “Image”), or providing any automation, design, resource,assistance, or other facilitation in the capturing of the Image, youagree that you have captured these Terms of Service and that youacknowledge and agree to them. If you cannot agree to these Terms ofService, you should immediately delete the captured Image. Failure to doso will constitute acceptance of these Terms of Service.

2. The User Jules Caesar owns all of the rights associated with theImage and any representation of any part of Jules Caesar thereof;

3. By capturing the Image, you agree to provide the User Jules Caesarjust compensation for any commercialization of the User's personalityrights that may be captured in the Image.

4. By capturing the Image, you agree to take all reasonable actions totrack the Image and to provide an accounting of all commercializationattempts related to the Image, whether successful or not.

5. By capturing the Image, you accept a Liquidated Damages agreement inwhich unauthorized use of the Image will result in mandatory damages ofat least, but not limited to, $1,000,000.

A privacy beacon may include, but is not limited to, one or more of amarker that reflects light in a visible spectrum, a marker that reflectslight in a nonvisible spectrum, a marker that emits light in a visiblespectrum, a marker that emits light in a nonvisible spectrum, a markerthat emits a radio wave, a marker that, when a particular type ofelectromagnetic wave hits it, emits a particular electromagnetic wave,an RFID tag, a marker that uses near-field communication, a marker thatis in the form of a bar code, a marker that is in the form of a bar codeand painted on a user's head and that reflects light in a nonvisiblespectrum, a marker that uses high frequency low penetration radio waves(e.g., 60 GHz radio waves), a marker that emits a particular thermalsignature, a marker that is worn underneath clothing and is detectableby an x-ray-type detector, a marker that creates a magnetic field, amarker that emits a sonic wave, a marker that emits a sonic wave at afrequency that cannot be heard by humans, a marker that is tattooed to aperson's bicep and is detectable through clothing, a marker that is apart of a user's cellular telephone device, a marker that is broadcastby a part of a user's cellular telephone device, a marker that isbroadcast by a keychain carried by a person, a marker mounted on a dronethat maintains a particular proximity to the person, a marker mounted ineyeglasses, a marker mounted in a hat. a marker mounted in an article ofclothing, the shape of the person's face is registered as the beacon, afeature of a person registered as the beacon, a marker displayed on ascreen, a marker in the form of an LED, a marker embedded on a page, ora book, a string of text or data that serves as a marker, a markerembedded or embossed onto a device, and the like.

FIGS. 12A-12C depict various implementations of operation 1102,depicting acquiring a captured image, wherein the captured image depictsat least one entity according to embodiments. Referring now to FIG. 12A,operation 1102 may include operation 1202 depicting acquiring thecaptured image, wherein the captured image depicts at least one featureof at least one entity. For example, FIG. 7, e.g., FIG. 7A showscaptured image that depicts a feature of the at least one entityreceiving module 702 acquiring the captured image (e.g., a picture ofthree friends at a birthday party), wherein the captured image (e.g.,the picture of three friends at the birthday party) depicts at least onefeature (e.g., a face) of at least one entity (e.g., one of the peoplein the picture of the three friends at the birthday party, e.g., who maybe associated with a privacy beacon configured to indicate to anyonethat captures her image that there is a terms of service associated withthe use of her image).

Referring again to FIG. 12A, operation 1202 may include operation 1204depicting acquiring the captured image, wherein the captured imagedepicts a face of a celebrity. For example, FIG. 7, e.g., FIG. 7A, showscaptured image that depicts a face of a celebrity receiving module 704acquiring the captured image (e.g., an image of five people at a homepoker game), wherein the captured image (e.g., the image of the fivepeople at the home poker game) depicts a face of a celebrity (e.g., aperson that is known by more than some percentage of the population,e.g., an actress, a football quarterback, a movie star, a famouspolitician).

Referring again to FIG. 12A, operation 1204 may include operation 1206depicting receiving, from an image capture device that captured thecaptured image, wherein the captured image depicts the face of thecelebrity. For example, FIG. 7, e.g., FIG. 7A, shows captured image thatdepicts a face of a celebrity receiving from an image capture devicethat captured the image module 706 receiving, from an image capturedevice (e.g., a wearable computer, e.g., a hypothetical Microsoft“KinectVision”) that captured the image (e.g., an image of two people ona first date at a bar), wherein the captured image (e.g., the image oftwo people on a first date at a bar) depicts the face of the celebrity(e.g., one of the people).

Referring again to FIG. 12A, operation 1206 may include operation 1208depicting receiving, from a wearable computer that covertly captured theimage, the captured image, wherein the captured image depicts the faceof the celebrity. For example, FIG. 7, e.g., FIG. 7A, shows capturedimage that depicts a face of a celebrity receiving from a wearablecomputer that covertly captured the image module 708 receiving, from awearable computer (e.g., an EyeTap device) that covertly captured thecaptured image (e.g., another patron at the bar took the picture withoutthe couple knowing about it), wherein the captured image (e.g., apicture of two people on a date at a restaurant) depicts the face of thecelebrity (e.g., one of the two people on the date is a famous Broadwayactress).

Referring again to FIG. 12A, operation 1102 may include operation 1210depicting acquiring the captured image from an image capture device,wherein the captured image depicts at least one entity. For example,FIG. 7, e.g., FIG. 7A, shows captured image that depicts at least oneentity receiving from an image capture device module 710 acquiring thecaptured image (e.g., an image of three people at a baseball game) froman image capture device (e.g., a stationary camera, e.g., a cameraembedded in the ATM machine next to the popcorn vendor), wherein thecaptured image depicts at least one entity (e.g., one of the threepeople at the baseball game).

Referring again to FIG. 12A, operation 1210 may include operation 1212depicting receiving the captured image from a wearable computer that isconfigured to covertly capture one or more images, wherein the capturedimage depicts at least one entity. For example, FIG. 7, e.g., FIG. 7A,shows captured image that depicts at least one entity receiving from awearable computer device that is configured to covertly capture one ormore images module 712 receiving the captured image (e.g., an image of ayouth soccer team playing out on the field) from a wearable computer(e.g., a Google Glass device being worn by the goalie of one of theteams) that is configured to covertly capture one or more images (e.g.,the image of the youth soccer team), wherein the captured image (e.g.,the image of a youth soccer team playing out on the field) depicts atleast one entity (e.g., one of the persons on the soccer field).

Referring now to FIG. 12B, operation 1102 may include operation 1214depicting receiving the captured image from a server, wherein thecaptured image depicts at least one entity. For example, FIG. 7, e.g.,FIG. 7B, shows captured image that depicts at least one entity receivingfrom a server module 714 receiving the captured image (e.g., an image oftwo people having dinner) from a server (e.g., a server that handlescaptured images from one or more image capture devices, or a server thathas decided that the captured image is not valuable enough to riskdecryption and release), wherein the captured image (e.g., the image oftwo people having dinner) depicts at least one entity (e.g., one of thepeople having dinner).

Referring again to FIG. 12B, operation 1214 may include operation 1216depicting receiving the captured image from the server that hasperformed one or more processes on the image, wherein the captured imagedepicts at least one entity. For example, FIG. 7, e.g., FIG. 7B, showscaptured image that depicts at least one entity receiving from a serverthat is configured to perform one or more processes on one or morecaptured images captured by an image capture device module 716 receivingthe captured image (e.g., an image of three friends doing shots at abar) from the server that has performed one or more processes (e.g., aretrieval process that retrieves terms of service that govern release ofimages that contain one of the people in the picture) on the image,wherein the captured image (e.g., the image of three friends doing shotsat a bar) depicts at least one entity (e.g., one of the people).

Referring again to FIG. 12B, operation 1216 may include operation 1218depicting receiving the captured image from the server that hasdetermined that a potential revenue generated from the captured image isless than a potential liability incurred from a distribution of thecaptured image, wherein the captured image depicts at least one entity.For example, FIG. 7, e.g., FIG. 7B, shows captured image that depicts atleast one entity receiving from a server that is configured to compare apotential revenue generated from distribution of the captured image to apotential liability incurred from the distribution of the captured imagemodule 718 receiving the captured image (e.g., an image of two people ata bus stop) from the server that has determined that a potential revenuegenerated (e.g., from added web traffic to a site that posted the image,from added advertisement clicks generated, from a sale of the image to acelebrity following website, etc.) from the captured image (e.g., theimage of two people at a bus stop) is less than a potential liability(e.g., damages from the person depicted in the picture, in a form oflegal recovery, or expenses derived from dealing with legal action orthreatened legal action) incurred from a distribution of the capturedimage (e.g., the image of two people at a bus stop), wherein thecaptured image (e.g., the image of two people at a bus stop) depicts atleast one entity (e.g., one of the people in the image).

Referring again to FIG. 12B, operation 1102 may include operation 1220depicting acquiring the captured image that has previously beenencrypted, wherein the captured image depicts at least one entity. Forexample, FIG. 7, e.g., FIG. 7B, shows captured image that has previouslybeen encrypted and that depicts at least one entity receiving module 720acquiring the captured image (e.g., five people around a blackjack tableat a casino) that has previously been encrypted, wherein the capturedimage (e.g., the five people around a blackjack table at the casino)depicts at least one entity (e.g., at least one person).

Referring again to FIG. 12B, operation 1220 may include operation 1222depicting acquiring the captured image that has previously beenencrypted by an image capture device, wherein the captured image depictsat least one entity. For example, FIG. 7, e.g., FIG. 7B, shows capturedimage that has previously been encrypted by an image capture device thatcaptured the image and that depicts at least one entity receiving module722 acquiring the captured image (e.g., an image of two women at thebeach) that has previously been encrypted by an image capture device(e.g., a wearable computer, e.g., a Google Glass device), wherein thecaptured image (e.g., the image of two women at the beach) depicts atleast one entity (e.g., one of the persons in the picture).

Referring again to FIG. 12B, operation 1222 may include operation 1224depicting acquiring the captured image that has previously beenencrypted by an image capture device in a manner configured to hinderdistribution of the image prior to distribution approval, wherein thecaptured image depicts at least one entity. For example, FIG. 7, e.g.,FIG. 7B, shows captured image that has previously been encrypted by animage capture device to hinder unauthorized distribution of the capturedimage that depicts at least one entity receiving module 724 acquiringthe captured image (e.g., an image of a Little League baseball teamplaying a game) that has previously been encrypted by an image capturedevice (e.g., a wearable computer, e.g., a hypothetical Apple“iGlasses”) in a manner configured to hinder distribution of the imageprior to distribution approval (e.g., encrypted so that the image can'tbe emailed, posted, distributed, modified, until a remote locationanalyzes and approves the image), wherein the captured image depicts atleast one entity (e.g., one person).

Referring again to FIG. 12B, operation 1220 may include operation 1226depicting acquiring the captured image that has previously beenencrypted through use of a unique device code associated with an imagecapture device configured to capture the captured image. For example,FIG. 7, e.g., FIG. 7B, shows captured image that has previously beenencrypted through use of a unique device code associated with an imagecapture device that captured the captured image that depicts at leastone entity receiving module 726 acquiring the captured image (e.g.,three friends at a bowling alley) that has previously been encryptedthrough use of a unique device code (e.g., a sixteen digit code)associated with an image capture device (e.g., a wearable computer,e.g., a hypothetical Samsung “Spectacles”) configured to capture thecaptured image (e.g., the image of three friends at a bowling alley).

Referring now to FIG. 12C, operation 1102 may include operation 1228depicting acquiring the captured image that contains a privacy beacon,wherein the captured image depicts at least one entity. For example,FIG. 7, e.g., FIG. 7C, shows captured image that depicts at least oneentity and that contains a privacy beacon receiving module 728 acquiringthe captured image that contains a privacy beacon (e.g., a marker thatemits light in a visible spectrum), wherein the captured image (e.g., aperson eating at a restaurant) depicts at least one entity (e.g., aperson associated with the privacy beacon, e.g., a celebrity).

Referring again to FIG. 12C, operation 1228 may include operation 1230depicting acquiring the captured image that contains a privacy beaconassociated with the entity depicted in the captured image. For example,FIG. 7, e.g., FIG. 7C, shows captured image that depicts at least oneentity and that contains a privacy beacon that is associated with thedepicted at least one entity receiving module 730 acquiring the capturedimage (e.g., a picture of three friends sitting courtside at aprofessional basketball game) that contains a privacy beacon (e.g., amarker that is tattooed to a person's bicep and is detectable throughclothing) associated with the entity (e.g., one of the basketballplayers in the background) depicted in the captured image (e.g., thepicture of three friends sitting courtside at a professional basketballgame).

Referring again to FIG. 12C, operation 1228 may include operation 1232depicting acquiring the captured image that contains a privacy beaconconfigured to be used to obtain data that regards the entity depicted inthe captured image. For example, FIG. 7, e.g., FIG. 7C, shows capturedimage that depicts at least one entity and that contains a privacybeacon that is configured to facilitate acquisition of data that regardsthe depicted entity receiving module 732 acquiring the captured image(e.g., a surreptitious picture of a person doing work at a coffee shop)that contains a privacy beacon (e.g., a marker that reflects light in avisible spectrum) configured to be used to obtain data (e.g., terms ofservice data that govern use of the person's likeness) that regards theentity (e.g., the person in the coffee shop) depicted in the capturedimage (e.g., the surreptitious picture of the person doing work in acoffee shop).

Referring again to FIG. 12C, operation 1232 may include operation 1234depicting acquiring the captured image that contains the privacy beaconconfigured to be used to obtain term data that includes one or moreterms of service that govern distribution of the captured image. Forexample, FIG. 7, e.g., FIG. 7C, shows captured image that depicts atleast one entity and that contains a privacy beacon that is configuredto facilitate acquisition of term data that includes one or more termsof service that govern distribution of the captured image that depictsthe at least one entity receiving module 734 acquiring the capturedimage (e.g., a picture of two people conducting a job interview in apublic place) that contains the privacy beacon (e.g., a marker that,when a particular type of electromagnetic wave hits it, emits aparticular electromagnetic wave) configured to be used to obtain termdata that includes one or more terms of service (e.g., a liquidateddamages clause) that govern distribution of the captured image (e.g.,the picture of two people conducting the job interview).

Referring again to FIG. 12C, operation 1228 may include operation 1236depicting acquiring the captured image that contains the privacy beaconthat was detected by an image capture device that captured the image.For example, FIG. 7, e.g., FIG. 7C, shows captured image that depicts atleast one entity and that contains a privacy beacon that was detected byan image capture device that captured the captured image receivingmodule 736 acquiring the captured image (e.g., a picture of a man and awoman on a date at a restaurant) that contains the privacy beacon (e.g.,marker that is broadcast by a part of a user's cellular telephonedevice) that was detected by an image capture device (e.g., ahypothetical wearable computer, e.g., an Apple “iGlasses) that capturedthe image (e.g., the picture of a man and a woman on a date at arestaurant).

FIGS. 13A-13D depict various implementations of operation 1104,depicting performing a comparison between an estimated potential revenuegenerated from a distribution of the captured image and an estimatedpotential liability incurred for the distribution of the captured image,according to embodiments. Referring now to FIG. 12A, operation 1004 mayinclude operation 1302 depicting performing the comparison between anestimated potential revenue generated from the distribution of thecaptured image to a social networking site, and an estimated potentialliability incurred for the distribution of the captured image. Forexample, FIG. 8, e.g., FIG. 8A, shows estimated potential revenuegenerated from captured image release to a social networking website andestimated potential liability created from captured image distributioncomparing module 802 performing the comparison between an estimatedpotential revenue (e.g., in terms of increased web traffic, and“reputation” to the user, which may be nonmonetary revenue) generatedfrom the distribution of the captured image (e.g., a picture of acelebrity eating at a fast food restaurant) to a social networking site(e.g., Twitter, Facebook, Myspace, Instagram, etc.), and an estimatedpotential liability (e.g., a risk that the celebrity will attempt torecover damages from the parties that allowed the distribution of theimage, as specified in a terms of service that was downloaded when aprivacy beacon associated with the celebrity was also detected in thepicture that was taken) incurred for the distribution (e.g., the“posting” to Facebook) of the captured image (e.g., the picture of acelebrity eating at a fast food restaurant).

Referring again to FIG. 13A, operation 1104 may include operation 1304depicting determining whether the estimated potential revenue generatedfrom a direct sale of the captured image is greater than the estimatedpotential liability incurred for the distribution of the captured image.For example, FIG. 8, e.g., FIG. 8A, shows estimated potential revenuegenerated from a direct sale of the captured image and the estimatedpotential liability created from captured image distribution comparingmodule 804 determining whether the estimated potential revenue generatedfrom a direct sale (e.g., to a sports gossip website, e.g., “Deadspin”)of the captured image (e.g., a picture of a superstar athlete drunk at abar and picking a fight with a fan) is greater than the estimatedpotential liability (e.g., from legal action taken by the superstarathlete or her representatives) incurred for the distribution of theimage (e.g., the picture of a superstar athlete drunk at a bar andpicking a fight with a fan).

Referring again to FIG. 13A, operation 1104 may include operation 1306depicting determining whether a ratio of the estimated potential revenuegenerated from the distribution of the captured image to the estimatedpotential liability incurred for the distribution of the captured imageis less than a particular number. For example, FIG. 8, e.g., FIG. 8A,shows amount of estimated potential revenue generated from capturedimage distribution to an amount of estimated potential liability createdfrom captured image distribution ratio evaluation module 806 determiningwhether a ratio of the estimated potential revenue (e.g., $500)generated (e.g., in terms of page views, increased clicks on ads,increased traffic to a web site) from the distribution (e.g., posting toa blog) of the captured image (e.g., a surreptitious picture of acelebrity standing next to a crashed car) to the estimated potentialliability (e.g., $50,000) incurred for the distribution (e.g., postingto a blog) of the captured image (e.g., a surreptitious picture of acelebrity standing next to a crashed car) is less than a particularnumber (e.g., 1:100).

Referring again to FIG. 13A, operation 1104 may include operation 1308depicting estimating a revenue value that corresponds to an estimatedpotential advertisement revenue generated from a distribution of thecaptured image. For example, FIG. 8, e.g., FIG. 8A, shows revenue valuethat corresponds to an estimated potential advertisement revenuegenerated from the distribution of the captured image estimating module808 estimating a revenue value (e.g., $800) that corresponds to anestimated potential advertisement revenue (e.g., from advertisements,either in the image or on a same page or Internet site as the image)generated from distribution of the captured image (e.g., an image of twopeople at a bar).

Referring again to FIG. 13A, operation 1104 may include operation 1310depicting obtaining an estimated damage value that corresponds to theestimated potential liability incurred for distribution of the capturedimage. For example, FIG. 8, e.g., FIG. 8A, shows estimated damage valuethat corresponds to the estimated potential liability incurred fordistribution of the captured image obtaining module 810 obtaining anestimated damage value (e.g., $1,000) that corresponds to the estimatedpotential liability incurred for distribution (e.g., e-mailing thepicture to a friend who posts it on her blog) of the captured image(e.g., the image of two people at a bar).

Referring again to FIG. 13A, operation 1104 may include operation 1312depicting performing a comparison between the obtained estimated damagevalue and the estimated revenue value. For example, FIG. 8, e.g., FIG.8A, shows comparison between the estimated revenue value and theobtained estimated damage value executing module 812 performing acomparison between the obtained estimated damage value (e.g., $1,000)and the estimated revenue value (e.g., $800).

Referring now to FIG. 13B, operation 1104 may include operation 1314depicting estimating a revenue value that corresponds to the estimatedpotential advertisement revenue generated from a distribution of thecaptured image. For example, FIG. 8, e.g., FIG. 8B, shows revenue valuethat corresponds to the estimated potential advertisement revenuegenerated from distribution of the captured image estimating module 814estimating a revenue value (e.g., $10,000) that corresponds to theestimated potential advertisement revenue (e.g., from advertisements andweb site hits) generated from a distribution of the captured image(e.g., a surreptitious image of a person waiting in line for theemergency room).

Referring again to FIG. 13B, operation 1104 may include operation 1316depicting performing a comparison between the estimated revenue valueand the estimated potential liability incurred for distribution of thecaptured image. For example, FIG. 8, e.g., FIG. 8B, shows comparisonbetween the estimated revenue value and the estimated potentialliability created from captured image distribution performing module 816performing a comparison between the estimated revenue value (e.g.,$10,000) and the estimated potential liability (e.g., $25,000, from aliquidated damages clause downloaded from a terms of service) incurredfor distribution of the captured image (e.g., the surreptitious image ofa person waiting in line for the emergency room).

Referring again to FIG. 13B, operation 1314 may include operation 1318depicting estimating the revenue value that corresponds to the estimatedpotential advertisement revenue generated from a post of the capturedimage to a social networking site. For example, FIG. 8, e.g., FIG. 8B,shows revenue value that corresponds to the estimated potentialadvertisement revenue generated from a post of the captured image to asocial network website estimating module 818 estimating the revenuevalue (e.g., $1,000) that corresponds to the estimated potentialadvertisement revenue (e.g., from increased traffic to that person'ssocial network site, in the form of “friends,” for Facebook, “follows,”for Twitter, and the like) generated from a post of the captured image(e.g., a surreptitious image of a famous person being pulled over forspeeding by the police) to a social networking site (e.g., Facebook,Twitter, Instagram, Pinterest, etc.).

Referring again to FIG. 13B, operation 1314 may include operation 1320depicting estimating the revenue value that corresponds to the estimatedpotential advertisement revenue generated from a post of the capturedimage to a photo share repository. For example, FIG. 8, e.g., FIG. 8B,shows revenue value that corresponds to the estimated potentialadvertisement revenue generated from a post of the captured image to aphoto repository estimating module 820 estimating the revenue value(e.g., $50) that corresponds to the estimated potential advertisementrevenue (e.g., in a form of clicks on an advertisement embedded in thepicture) generated from a post of the captured image to a photo sharerepository (e.g., a shared drive for photos, e.g., through MicrosoftSkyDrive or DropBox).

Referring again to FIG. 13B, operation 1314 may include operation 1322depicting estimating the revenue value that corresponds to the estimatedpotential advertisement revenue generated from a post of the capturedimage to a themed web site. For example, FIG. 8, e.g., FIG. 8B, showsestimating the revenue value that corresponds to the estimated potentialadvertisement revenue generated from a post of the captured image to athemed web site 822 estimating the revenue value that corresponds to theestimated potential advertisement revenue generated from a post of thecaptured image (e.g., an image of two people riding motorcycles) to athemed website (e.g., a motorcycle enthusiast site).

Referring again to FIG. 13B, operation 1322 may include operation 1324depicting estimating the revenue value that corresponds to the estimatedpotential advertisement revenue generated from one or more clicks on anadvertisement link on the website to which the captured image is posted.For example, FIG. 8, e.g., FIG. 8B, shows revenue value that correspondsto the estimated potential advertisement revenue generated from one ormore clicks on an advertisement link on the themed website to which thecaptured image is posted estimating module 824 estimating the revenuevalue that corresponds to the estimated potential advertisement revenuegenerated from one or more clicks on an advertisement link on thewebsite (e.g., a celebrity gossip website) to which the captured imageis posted.

Referring again to FIG. 13B, operation 1322 may include operation 1326depicting estimating the revenue value that corresponds to the estimatedpotential advertisement revenue generated from one or more purchases ofa product and/or service after a view of the advertisement on thewebsite to which the captured image is posted. For example, FIG. 8,e.g., FIG. 8B, shows the revenue value that corresponds to the estimatedpotential advertisement revenue generated from one or more productpurchases after a view of an advertisement on the themed website towhich the captured image is posted estimating module 826 estimating therevenue value that corresponds to the estimated potential advertisementrevenue generated from one or more purchases of a product and/or service(e.g., a particular brand of tequila) after a view of the advertisementon the website (e.g., a celebrity fan site) to which the captured image(e.g., a surreptitiously-taken picture of the celebrity drinking theparticular brand of tequila) is posted.

Referring again to FIG. 13B, operation 1322 may include operation 1328depicting estimating the revenue value that corresponds to the estimatedpotential advertisement revenue generated from one or more clicks on anadvertisement link inserted into the captured image. For example, FIG.8, e.g., FIG. 8B, shows the revenue value that corresponds to theestimated potential advertisement revenue generated from one or moreclicks on an advertisement image inserted into the captured image postedto the themed web site estimating module 828 estimating the revenuevalue that corresponds to the estimated potential advertisement revenuegenerated from one or more clicks on an advertisement link inserted intothe captured image (e.g., an image of a man working at his computer in asemi-public place (e.g., an airport terminal)).

Referring now to FIG. 13C, operation 1322 may include operation 1330depicting estimating the revenue value that corresponds to the estimatedpotential advertisement revenue generated from increased traffic to thewebsite to which the captured image is posted. For example, FIG. 8,e.g., FIG. 8C, shows the revenue value that corresponds to the estimatedpotential advertisement revenue generated through increased traffic tothe themed website to which the captured image is posted estimatingmodule 830 estimating the revenue value that corresponds to theestimated potential advertisement revenue generated from increasedtraffic to the website to which the captured image (e.g., an image of aperson on a riding lawn mower) is posted.

Referring now to FIG. 13D, operation 1104 may include operation 1332depicting obtaining an estimated damage value that corresponds to theestimated potential liability incurred for distribution of the capturedimage. For example, FIG. 8, e.g., FIG. 8D, shows estimated damage valuethat corresponds to the estimated potential liability created fromcaptured image distribution obtaining module 832 obtaining an estimateddamage value (e.g., $1000) that corresponds to the estimated potentialliability incurred for distribution of the captured image (e.g., animage of two people having coffee at a donut shop).

Referring again to FIG. 13D, operation 1104 may include operation 1334depicting performing a comparison between the estimated damage value andthe estimated potential revenue generated from the distribution of thecaptured image. For example, FIG. 8, e.g., FIG. 8D, shows comparisonbetween the obtained estimated damage value and the estimated potentialrevenue generated from captured image distribution performing module 834performing a comparison between the estimated damage value (e.g.,$1,000) and the estimated potential revenue (e.g., $1,000) generatedfrom the distribution of the captured image (e.g., an image of a persondrinking a sports drink).

Referring again to FIG. 13D, operation 1332 may include operation 1336depicting obtaining the estimated damage value that corresponds to theestimated potential liability incurred for distribution of the capturedimage at least partly based on term data that includes one or more termsof service that govern the distribution of the image. For example, FIG.8, e.g., FIG. 8D, shows estimated damage value that corresponds to theestimated potential liability created from captured image distributionobtaining at least partly based on term data that includes one or moreterms of service that govern distribution of the captured image module836 obtaining the estimated damage value (e.g., based on a liquidateddamages clause that specifies 1,000,000 dollars in damages for use ofthe image as a deterrent, and multiplying that by a likelihood factor,e.g., how likely it is that this clause would be enforced orenforceable) that corresponds to the estimated potential liabilityincurred for distribution (e.g., emailing to reporters who may post theimage into one or more newspapers) of the captured image (e.g., an imageof a celebrity eating at a restaurant that was taken without thatcelebrity's knowledge) at least partly based on term data that includesone or more terms of service (e.g., the million dollar liquidateddamages clause) that govern the distribution of the image (e.g., theimage of the celebrity eating at a restaurant).

Referring again to FIG. 13D, operation 1336 may include operation 1338depicting obtaining the estimated damage value that corresponds to theestimated potential liability incurred for distribution of the capturedimage at least partly based on term data that includes one or more termsof service that include a liquidated damages clause that applies todistribution of the captured image. For example, FIG. 8, e.g., FIG. 8D,shows estimated damage value that corresponds to the estimated potentialliability created from captured image distribution obtaining at leastpartly based on term data that includes one or more terms of servicethat specify a liquidated damages clause that applies to distribution ofthe captured image module 838 obtaining the estimated damage value(e.g., 28,000 dollars) that corresponds to the estimated potentialliability incurred for distribution of the captured image (e.g., animage of three professional football players on a boat on a Minnesotalake) at least partly based on term data that includes one or more termsof service that include a liquidated damages clause that applies todistribution of the image (e.g., an image of three professional footballplayers on a boat on a Minnesota lake).

Referring again to FIG. 13D, operation 1332 may include operation 1340depicting obtaining the estimated damage value that corresponds to theestimated potential liability incurred for distribution of the capturedimage at least partly based on privacy metadata related to the capturedimage. For example, FIG. 8, e.g., FIG. 8D, shows estimated damage valuethat corresponds to the estimated potential liability created fromcaptured image distribution that is at least partly based on privacymetadata related to the captured image obtaining module 840 obtainingthe estimated damage value (e.g., 100,000 dollars) that corresponds tothe estimated potential liability incurred (e.g., indirect costsincurred for defending against the possibility of legal action, e.g.,purging old data, keeping a lawyer on retainer, etc.) for distributionof the captured image (e.g., an image of a famous sub sandwichspokesperson eating a cheeseburger) at least partly based on privacymetadata (e.g., an identifier of the entity that is protected, e.g.,with a privacy beacon) related to the captured image (e.g., the image ofa famous sub sandwich spokesperson eating a cheeseburger).

Referring again to FIG. 13D, operation 1340 may include operation 1342depicting obtaining the estimated damage value that corresponds to theestimated potential liability incurred for distribution of the capturedimage at least partly based on privacy metadata that identifies theentity depicted in the captured image. For example, FIG. 8, e.g., FIG.8D, shows estimated damage value that corresponds to the estimatedpotential liability created from captured image distribution that is atleast partly based on privacy metadata that identifies the depictedentity in the captured image obtaining module 842 obtaining theestimated damage value that corresponds to the estimated potentialliability incurred for distribution of the captured image (e.g., animage of three people doing shots at a bar) at least partly based onprivacy metadata that identifies the entity (e.g., “this is JuliusCaesar) depicted in the captured image (e.g., the image of three peopledoing shots at a bar).

Referring now to FIG. 13E, operation 1332 may include operation 1344depicting obtaining a privacy metadata related to the captured image.For example, FIG. 8, e.g., FIG. 8E, shows privacy metadata related tothe captured image acquiring module 844 obtaining a privacy metadatarelated to the captured image (e.g., the privacy metadata identifiesthat one of the people depicted in the captured image has a privacybeacon associated with that person, and identifies that person and howto obtain terms of service that govern the distribution of images thatcontain the depiction of that person).

Referring again to FIG. 13E, operation 1332 may include operation 1346depicting generating an estimated damage value that corresponds to theestimated potential liability incurred for distribution of the capturedimage, at least partly based on the obtained privacy metadata. Forexample, FIG. 8, e.g., FIG. 8E, shows estimated damage value thatcorresponds to the estimated potential liability created from capturedimage distribution at least partly based on the acquired privacymetadata obtaining module 846 generating an estimated damage value(e.g., $10,000) that corresponds to the estimated potential liability(e.g., damages that a person might recover) incurred for distribution ofthe captured image (e.g., an image of two people with sideline passesfor a Monday Night Football professional football game), at least partlybased on the obtained privacy metadata (e.g., an identifier of theprivacy beacon detected in the image that is associated with one of thepeople depicted in the image).

Referring again to FIG. 13E, operation 1344 may include operation 1348depicting obtaining a privacy metadata related to a privacy beaconassociated with the entity depicted in the captured image. For example,FIG. 8, e.g., FIG. 8E, shows privacy metadata related to a privacybeacon linked to an entity depicted in the captured image acquiringmodule 848 obtaining a privacy metadata (e.g., regarding an existence ofa privacy beacon) related to a privacy beacon (e.g., a marker that is inthe form of a bar code) associated with the entity depicted in thecaptured image (e.g., an image of a person acting as a celebrity chef ata fancy restaurant).

Referring again to FIG. 13E, operation 1348 may include operation 1350depicting obtaining the privacy metadata related to a privacy beaconassociated with the depicted entity and that was detected in thecaptured image. For example, FIG. 8, e.g., FIG. 8E, shows privacymetadata related to the privacy beacon linked to the entity depicted inthe captured image and that was detected in the captured image acquiringmodule 850 obtaining the privacy metadata related to a privacy beaconassociated with the depicted entity (e.g., one of the persons in thecaptured image) and that was detected in the captured image (e.g., animage of three people at a baseball game).

Referring again to FIG. 13E, operation 1344 may include operation 1352depicting obtaining the privacy metadata related to the privacy beaconthat identifies the entity depicted in the captured image. For example,FIG. 8, e.g., FIG. 8E, shows privacy metadata related to a privacybeacon that identifies an entity depicted in the captured imageacquiring module 852

Referring now to FIG. 13F, operation 1344 may include operation 1354depicting obtaining the privacy metadata that is configured tofacilitate retrieval of term data that regards the entity depicted inthe captured image. For example, FIG. 8, e.g., FIG. 8F, shows privacymetadata configured to facilitate retrieval of term data that regardsthe entity depicted in the captured image acquiring module 854 obtainingthe privacy metadata (e.g., an identifier that a privacy beacon wasdetected and a web address at which terms of service that governentities associated with that privacy beacon is detected) that isconfigured to facilitate retrieval of term data that regards the entitydepicted in the captured image (e.g., an image of four friends at anamusement park).

Referring again to FIG. 13F, operation 1354 may include operation 1356depicting obtaining the privacy metadata that is an identification codeof a privacy beacon associated with the entity depicted in the capturedimage and that is configured to facilitate the retrieval of the termdata. For example, FIG. 8, e.g., FIG. 8F, shows privacy metadata that isan identification code of a privacy beacon associated with the entitydepicted in the captured image and that is configured to facilitateretrieval of the term data acquiring module 856 obtaining the privacymetadata that is an identification code of a privacy beacon associatedwith the entity (e.g., a person) depicted in the captured image (e.g.,an image of two people on a first date at a café) and that is configuredto facilitate the retrieval of the term data (e.g., data that governsdistribution of the image that contains the entity for which the privacybeacon is associated, e.g., the woman at the café on the first date).

Referring again to FIG. 13F, operation 1356 may include operation 1358depicted obtaining the privacy metadata that is the identifier of theprivacy beacon associated with the entity and that is configured tofacilitate the retrieval of the term data from a term data databasestored at a remote location. For example, FIG. 8, e.g., FIG. 8F, showsprivacy metadata that is an identification code of a privacy beaconassociated with the entity depicted in the captured image and that isconfigured to facilitate retrieval of the term data from a databaseacquiring module 858 obtaining the privacy metadata that is theidentifier of the privacy beacon (e.g., marker that emits light in anonvisible spectrum) associated with the entity and that is configuredto facilitate the retrieval of the term data from a term data databasestored at a remote location (e.g., a server that manages term data formultiple people that is owned by the same entity that provides theprivacy beacons).

Referring again to FIG. 13F, operation 1354 may include operation 1360depicting obtaining the privacy metadata that is configured tofacilitate retrieval of term data that includes one or more terms ofservice regarding distribution of the captured image that depicts theentity. For example, FIG. 8, e.g., FIG. 8F, shows privacy metadataconfigured to facilitate retrieval of term data that includes one ormore terms of service that regard distribution of captured image thatdepicts the entity acquiring module 860 obtaining the privacy metadata(e.g., a key value that can be used to query a database to retrieve aterms of service) that is configured to facilitate retrieval of termdata that includes one or more terms of service (e.g., a full-profitsclause regarding any sale of the image) regarding distribution (e.g.,sale, posting to a website, emailing, etc.) of the captured image (e.g.,an image of a person water skiing) that depicts the entity (e.g., thewater skier).

Referring now to FIG. 13G, operation 1104 may include operation 1362depicting estimating an advertisement revenue value that corresponds tothe estimated potential advertisement revenue generated from thedistribution of the captured image with an advertisement placed in thecaptured image. For example, FIG. 8, e.g., FIG. 8G shows estimatedpotential advertisement revenue generated from distribution of capturedimage that includes an advertisement placed in the captured imagedistribution comparing module 862 estimating an advertisement revenuevalue that corresponds to the estimated potential advertisement revenuegenerated from the distribution of the captured image (e.g., an image oftwo friends having dinner) with an advertisement (e.g., a message fromthe dairy lobby to drink milk) placed in the captured image (e.g., theimage of two friends having dinner).

Referring again to FIG. 13G, operation 1104 may include operation 1364depicting performing a comparison between the estimated advertisementrevenue value and the estimated potential liability incurred fordistribution of the captured image. For example, FIG. 8, e.g., FIG. 8G,shows estimated potential advertisement revenue and estimated potentialliability created from captured image distribution comparing module 864performing a comparison between the estimated advertisement revenuevalue (e.g., $1,000) and the estimated potential liability incurred fordistribution of the captured image (e.g., an image of a row of people onan airplane that was surreptitiously taken with a wearable computer).

Referring again to FIG. 13G, operation 1362 may include operation 1366depicting estimating the advertisement revenue value that corresponds tothe estimated potential advertisement revenue generated from thedistribution of the captured image with the advertisement placed in thecaptured image at a position away from the depicted entity. For example,FIG. 8, e.g., FIG. 8G, shows potential advertisement revenue generatedfrom distribution of captured image that includes an advertisementplaced in the captured image at a position away from the depicted entitydistribution estimating module 866 estimating the advertisement revenuevalue that corresponds to the estimated potential advertisement revenuegenerated from the distribution of the captured image with theadvertisement (e.g., an ad for cat food) placed in the captured image(e.g., an image of a person walking their dog taken covertly) at aposition away from the depicted entity (e.g., the person who is walkingtheir dog).

FIGS. 14A-14E depict various implementations of operation 1106,depicting modifying the captured image by placing at least oneadvertisement image at a particular location of the captured image,according to embodiments. Referring now to FIG. 14A, operation 1106 mayinclude operation 1402 depicting modifying the captured image by placingthe at least one advertisement image at the particular location of thecaptured image, wherein the particular location of the at least oneadvertisement image is at least partially based on the performedcomparison. For example, FIG. 9, e.g., FIG. 9A, shows captured imagemodification through placement of the at least one advertisement imageat the particular location into the captured image facilitating module902 modifying the captured image (e.g., an image of five people at ahome poker game) by placing the at least one advertisement image (e.g.,an advertisement for an online poker website that pays to be insertedinto any picture that has a celebrity on a particular list) at theparticular location (e.g., not over a face and/or body of any entity) ofthe captured image (e.g., the image of five people at a home pokergame), wherein the particular location of the at least one advertisementimage (e.g., an advertisement for an online poker website that pays tobe inserted into any picture that has a celebrity on a particular list)is at least partially based on the performed comparison (e.g., if therevenue generated is too low, or the liability too high, or thedifference between the two is too great, the particular location may beset to cover at least a portion of the protected entity).

Referring again to FIG. 14A, operation 1402 may include operation 1404depicting modifying the captured image by placing at least oneadvertisement image at the particular location of the captured image,wherein the particular location of the at least one advertisement imageis at least partially based on a result of the performed comparison. Forexample, FIG. 9, e.g., FIG. 9A, shows captured image modificationthrough placement of the at least one advertisement image into thecaptured image at the particular location that is at least partiallydetermined by one or more of the estimated potential revenue and theestimated potential liability facilitating module 904 modifying thecaptured image (e.g., an image of four fighter pilots playing beachvolleyball) by placing at least one advertisement image (e.g., anadvertisement for deodorant) at the particular location (e.g., over oneof the fighter pilots' heads) of the captured image (e.g., the image offour fighter pilots playing beach volleyball), wherein the particularlocation (e.g., over one of the fighter pilot's heads) is at leastpartially based on a result of the performed comparison (e.g., theadvertisement will be positioned to cover one of the fighter pilot'sheads if the revenue is too low, in order to encourage people to clickon the advertisement to remove it).

Referring again to FIG. 14A, operation 1402 may include operation 1406depicting modifying the captured image by placing the at least oneadvertisement image at the particular location of the captured image,wherein a distance from the particular location to a center of thecaptured image is proportional to a size of the comparison between theestimated potential revenue and the estimated potential liability. Forexample, FIG. 9, e.g., FIG. 9A, shows captured image modificationthrough placement of the at least one advertisement image into thecaptured image at the particular location that has a distance from acenter of the captured image that is proportional to a differencebetween the estimated potential revenue and the estimated potentialliability facilitating module 906 modifying the captured image (e.g., astaged shot of two people having dinner at a seaside restaurant) byplacing the at least one advertisement image (e.g., an advertisement foran online dating service) at the particular location (e.g., close to thecenter of the picture), wherein a distance from the particular locationto a center of the captured image is proportional to a size of thecomparison between the estimated potential revenue and the estimatedpotential liability (e.g., the larger the shortfall between estimatedpotential revenue and estimated potential liability, the closer to thecenter the image will be, to try and maximize revenue).

Referring again to FIG. 14A, operation 1406 may include operation 1408depicting modifying the captured image by placing the at least oneadvertisement image at the particular location of the captured image,wherein a distance from the particular location to a center of thecaptured image is decreased or increased relative to a differencebetween the estimated potential revenue and the estimated potentialliability. For example, FIG. 9, e.g., FIG. 9A, shows captured imagemodification through placement of the at least one advertisement imageinto the captured image at the particular location that has a distancefrom a center of the captured image that is decreased as a differencebetween the estimated potential revenue and the estimated potentialliability is decreased facilitating module 908 modifying the capturedimage (e.g., an image of four friends at a baseball game) by placing theat least one advertisement image (e.g., an advertisement for aparticular brand of smoked sausages) at the particular location (e.g.,covering a face of one of the people in the picture) of the capturedimage (e.g., the image of four friends at the baseball game), wherein adistance from the particular location to a center of the captured imageis decreased or increased relative to a difference between the estimatedpotential revenue and the estimated potential liability.

Referring again to FIG. 14A, operation 1402 may include operation 1410depicting modifying the captured image by placing at least oneadvertisement image at the particular location, wherein the particularlocation overlaps the depicted entity in the captured image when theestimated potential revenue is less than the estimated potentialliability. For example, FIG. 9, e.g., FIG. 9A, shows captured imagemodification through placement of the at least one advertisement imageinto the captured image at the particular location that overlaps thedepicted entity when the estimated potential revenue is less than theestimated potential liability facilitating module 910 modifying thecaptured image (e.g., an image of a person at the beach, takensurreptitiously) by placing at least one advertisement image at theparticular location (e.g., covering a portion of the person's body),wherein the particular location overlaps the depicted entity (e.g., theperson) in the captured image(e.g., the image of the person at thebeach) when the estimated potential revenue is less than the estimatedpotential liability (e.g., covering a portion of the person may decreasethe potential liability (depending on the terms of service), and mayincrease potential revenue by encouraging the viewer to click on theadvertisement).

Referring again to FIG. 14A, operation 1410 may include operation 1412depicting modifying the captured image by placing the at least oneadvertisement image at the particular location, wherein the particularlocation overlaps a percentage of the depicted entity, and saidpercentage is linked to an amount that the estimated potential revenueis less than the estimated potential liability. For example, FIG. 9,e.g., FIG. 9A, shows captured image modification through placement ofthe at least one advertisement image into the captured image at theparticular location that overlaps a percentage of the depicted entitythat is proportional to an amount by which the estimated potentialrevenue is less than the estimated potential liability facilitatingmodule 912 modifying the captured image (e.g., an image of three peopleat a college football tailgate) by placing the at least oneadvertisement image at the particular location (e.g., covering 70% ofone of the people at the tailgate), wherein the particular locationoverlaps a percentage of the depicted entity (e.g., one of the people atthe tailgate), and said percentage is linked to (e.g., proportional to,or is one factor in a decision regarding) an amount that the estimatedpotential revenue is less than the estimated potential liability).

Referring now to FIG. 14B, operation 1106 may include operation 1414depicting modifying the captured image by placing at least oneadvertisement image at a particular location that covers the depictedentity in the captured image, when the estimated potential liability isgreater than a particular amount. For example, FIG. 9, e.g., FIG. 9B,shows at least one advertisement image inserting into the captured imageat a particular location that covers the depicted entity in the capturedimage when the estimated potential liability is greater than aparticular amount module 914 modifying the captured image (e.g., acovertly-taken image of a man sitting at a bar drinking scotch) byplacing at least one advertisement image (e.g., an advertisement forlawn fertilizer) at a particular location that covers the depictedentity (e.g., the man) in the captured image (e.g., the image of the mansitting at the bar drinking scotch), when the estimated potentialliability is greater than a particular amount (e.g., it may be necessaryto cover the man's face to avoid liability if the potential liabilityfor using the man's face, as specified in a downloaded terms of service,is too high).

Referring again to FIG. 14B, operation 1106 may include operation 1416depicting modifying the captured image by placing at least oneadvertisement image at a particular location that covers the depictedentity in the captured image, when the estimated potential revenue isless than a certain amount. For example, FIG. 9, e.g., FIG. 9B, shows atleast one advertisement image inserting into the captured image at aparticular location that covers the depicted entity in the capturedimage when the estimated potential revenue is less than a particularamount module 916 modifying the captured image (e.g., an image of twopeople on a sailboat) by placing at least one advertisement image (e.g.,an advertisement for macaroni & cheese) at a particular location thatcovers the depicted entity (e.g., one of the people on the sailboat) inthe captured image (e.g., the image of two people on a sailboat), whenthe estimated potential revenue is less than a certain amount (e.g., theadvertisement is added over top of the person's face, because otherwisethe picture isn't “worth” distributing because the potential revenue isso low).

Referring again to FIG. 14B, operation 1106 may include operation 1418depicting modifying the captured image by placing at least oneadvertisement image at a particular location that overlaps the depictedentity in the captured image. For example, FIG. 9, e.g., FIG. 9B, showscaptured image modification through placement of the at least oneadvertisement image into the captured image at the particular locationthat overlaps the depicted entity in the captured image facilitatingmodule 918 modifying the captured image (e.g., an image of four peopleat a Broadway play) by placing at least one advertisement image (e.g.,for tuxedo rental) at a particular location that overlaps the depictedentity (e.g., one of the people) in the captured image (e.g., the imageof the four people at the Broadway play).

Referring again to FIG. 14B, operation 1418 may include operation 1420depicting modifying the captured image by placing at least oneadvertisement image at a particular location that hides at least onefeature of the depicted entity in the captured image. For example, FIG.9, e.g., FIG. 9B, shows captured image modification through placement ofthe at least one advertisement image into the captured image at theparticular location that obscures at least one feature of the depictedentity in the captured image facilitating module 920 modifying thecaptured image (e.g., a surreptitious image of three college studentsstudying at the library) by placing at least one advertisement image(e.g., for an energy drink) in a particular location that hides at leastone feature (e.g., face, body, etc.) of the depicted entity (e.g., oneof the students, who is also a moderately famous reality televisionstar) in the captured image (e.g., the surreptitious image of threecollege students studying at the library).

Referring again to FIG. 14B, operation 1418 may include operation 1422depicting modifying the captured image by placing at least oneadvertisement image at the particular location that completely coversthe depicted entity in the captured image. For example, FIG. 9, e.g.,FIG. 9B, shows captured image modification through placement of the atleast one advertisement image into the captured image at the particularlocation that completely covers the depicted entity in the capturedimage facilitating module 922 modifying the captured image (e.g., asurreptitious image of people at a wedding) by placing at least oneadvertisement image (e.g., a dating service advertisement) at theparticular location that completely covers the depicted entity (e.g.,one of the bridesmaids at the wedding, who is an actress) in thecaptured image (e.g., the surreptitious image of people at a wedding).

Referring now to FIG. 14C, operation 1106 may include operation 1424modifying the captured image by placing a removable advertisement imageat the particular location of the captured image. For example, FIG. 9,e.g., FIG. 9B, shows captured image modification through placement of aremovable advertisement image at the particular location into thecaptured image facilitating module 924 modifying the captured image(e.g., an image of a politician at a charity fundraiser) by placing aremovable (e.g. an advertisement that goes away after a particularperiod of time, or after a user action, or after a command is issued bythe device displaying the advertisement) advertisement image (e.g., anadvertisement for a particular brand of light beer) at the particularlocation of the captured image (e.g., the image of the politician at thecharity fundraiser).

Referring again to FIG. 14C, operation 1424 may include operation 1426depicting modifying the captured image by placing the removableadvertisement image at the particular location of the captured imagethat overlaps the depicted entity. For example, FIG. 9, e.g., FIG. 9C,shows captured image modification through placement of a removableadvertisement image into the captured image at the particular locationthat overlaps the depicted entity facilitating module 926 modifying thecaptured image (e.g., an image of five people at a home poker game) byplacing the removable advertisement image (e.g., an advertisement fordog food that goes away when a pointer hovers over it) at the particularlocation (e.g., overlapping one of the famous people in the picture) ofthe captured image (e.g., the image of five people at the home pokergame) that overlaps the depicted entity (e.g., one of the famous peoplein the picture).

Referring again to FIG. 14C, operation 1424 may include operation 1428depicting modifying the captured image by placing aremovable-on-selection advertisement image at the particular location ofthe captured image. For example, FIG. 9, e.g., FIG. 9C, shows capturedimage modification through placement of a removable advertisement imageinto the captured image at the particular location that is configured tobe removed in response to a mouse click facilitating module 928modifying the captured image (e.g., an image of three women at a beachin Mexico) by placing a removable-on-selection (e.g., the ad is removedwhen clicked on by a mouse pointer or selected with a finger on atouchscreen, etc.) advertisement image (e.g., an advertisement foronline sports betting) at the particular location (e.g., over a body ofone of the women) of the captured image (e.g., the image of three womenat a beach in Mexico).

Referring again to FIG. 14C, operation 1424 may include operation 1430depicting modifying the captured image by placing aremovable-on-mouseover advertisement image at the particular location ofthe captured image. For example, FIG. 9, e.g., FIG. 9C, shows capturedimage modification through placement of a removable advertisement imageinto the captured image at the particular location that is configured tobe removed in response to a mouse over the removable advertisement imagefacilitating module 930 modifying the captured image (e.g., an image oftwo men sitting in a Ferrari) by placing a removable-on-mouseover (e.g.,which, in this context, includes any pointer or “touch”-over, e.g., witha finger) advertisement image (e.g., the image of two men sitting in aFerrari) at the particular location of the captured image (e.g., overone of the men's faces).

Referring now to FIG. 14D, operation 1106 may include operation 1432depicting modifying the captured image by generating a new image thatincludes the captured image and at least one advertisement image at theparticular location of the captured image. For example, FIG. 9, e.g.,FIG. 9D, shows new image that includes the captured image and at leastone advertisement image at the particular location generating module 932modifying the captured image (e.g., an image of four women playinghockey) by generating a new image (e.g., a copy of the captured imagethat adds the advertisement and leaves the original captured imageintact) that includes the captured image (e.g., an image of two womencanoeing) and at least one advertisement image (e.g., an advertisementfor outdoor gear) at the particular location of the captured image(e.g., the image of two women canoeing).

Referring now to FIG. 14D, operation 1106 may include operation 1434depicting modifying the captured image by overlaying the at least oneadvertisement image at the particular location of the captured image.For example, FIG. 9, e.g., FIG. 9D, shows at least one advertisementimage addition by overlaying the at least one advertisement image intothe captured image at the particular location module 934 modifying thecaptured image (e.g., an image of three friends at a hockey game, one ofwhom is a professional athlete in a different sport in a different city)by overlaying the at least one advertisement image (e.g., anadvertisement for sports jerseys) at the particular location (e.g.,overlapping a portion of one of the person's faces) of the capturedimage (e.g., the image of three friends at a hockey game, one of whom isa professional athlete in a different sport in a different city).

Referring again to FIG. 14D, operation 1106 may include operation 1436depicting modifying the captured image by overwriting the particularlocation of the captured image with the at least one advertisementimage. For example, FIG. 9, e.g., FIG. 9D, shows at least oneadvertisement image insertion into the captured image by overwriting thecaptured image at the particular location module 936 modifying thecaptured image by overwriting the particular location of the capturedimage with the at least one advertisement image (e.g., an advertisementfor subscriptions to magazines).

FIGS. 15A-15B depict various implementations of operation 1108,depicting calculating an updated estimated potential revenue from themodified captured image that includes the at least one advertisementimage at the particular location, according to embodiments. Referringnow to FIG. 15A, operation 1108 may include operation 1502 depictingestimating an updated potential revenue from the modified captured imagethat includes the at least one advertisement image at the particularlocation. For example, FIG. 10, e.g., FIG. 10A, shows updated estimatedpotential revenue from the captured image with the inserted at least oneadvertisement image at the particular location projecting module 1002estimating an updated potential revenue from the modified captured image(e.g., an image of three people at a restaurant that has anadvertisement for a tax return preparation service because the image wastaken during tax season) that includes the at least one advertisementimage (e.g., an advertisement for a tax return preparation service) atthe particular location (e.g., in a center of the image). It is notedthat, in some embodiments, the placed advertisement may be related tothe content of the image. In other embodiments, the placed advertisementmay be related to a feature of the image, e.g., as above, theadvertisement is based on a date the image was taken, although otherattributes of the image could be used. In another embodiment, theadvertisement may be selected randomly.

Referring again to FIG. 15A, operation 1502 may include operation 1504depicting estimating the updated potential revenue from the modifiedcaptured image that includes the at least one advertisement imagethrough estimation of a number of clicks on the at least oneadvertisement image. For example, FIG. 10, e.g., FIG. 10A, shows updatedestimated potential revenue from the captured image with the inserted atleast one advertisement image at the particular location projecting atleast partly based on an estimated number of clicks on the at least oneadvertisement image module 1004 estimating the updated potential revenuefrom the modified captured image (e.g., an image of a youth soccer teamthat has been modified to include an advertisement for sporting goodsequipment) that includes the at least one advertisement image (e.g., theadvertisement for sporting goods equipment) through estimation of anumber of clicks (e.g., 5% of the total views, e.g., 100 total clicks)on the at least one advertisement image (e.g., the advertisement forsporting goods equipment).

Referring again to FIG. 15A, operation 1108 may include operation 1506depicting adding an estimated revenue from the at least oneadvertisement image to at least a portion of the estimated potentialrevenue. For example, FIG. 10, e.g., FIG. 10A, shows updated estimatedpotential revenue from the captured image with the inserted at least oneadvertisement image at the particular location calculating by adding anestimated revenue from the at least one advertisement image to at leasta portion of the estimated potential revenue module 1006 adding anestimated revenue (e.g., 350 dollars) from the at least oneadvertisement image (e.g., an advertisement for a particular brand ofvodka) to at least a portion of the estimated potential revenue (e.g.,1,000 dollars for the original image of three famous people sitting at abar drinking vodka).

Referring again to FIG. 15A, operation 1108 may include operation 1508depicting multiplying the estimated potential revenue by a particularmodifier related to the modified captured image. For example, FIG. 10,e.g., FIG. 10A, shows updated estimated potential revenue from thecaptured image with the inserted at least one advertisement image at theparticular location calculating by multiplication of the estimatedpotential revenue by a particular modifier module 1008 multiplying theestimated potential revenue (e.g., five thousand dollars) by aparticular modifier (e.g., 0.85×) related to the modified captured image(e.g., if the image is modified to hide the entity associated with theprivacy beacon with an advertisement, that may change the modifier formore or less revenue).

Referring again to FIG. 15A, operation 1508 may include operation 1510depicting multiplying the estimated potential revenue by a particularmodifier related to the entity depicted in the captured image. Forexample, FIG. 10, e.g., FIG. 10A, shows updated estimated potentialrevenue from the captured image with the inserted at least oneadvertisement image at the particular location calculating bymultiplication of the estimated potential revenue by an entity identityclassification modifier module 1010 multiplying the estimated potentialrevenue (e.g., 2,000 dollars) by a particular modifier (e.g., a 1.5×modifier) related to the entity (e.g., related to a characteristic ofthe entity, e.g., how famous the entity is, e.g., an identification dataof the entity, etc.) depicted in the captured image (e.g., an image of aperson sitting in a Ferrari).

Referring again to FIG. 15A, operation 1508 may include operation 1512depicting multiplying the estimated potential revenue by a particularmodifier related to the at least one advertisement image. For example,FIG. 10, e.g., FIG. 10A, shows updated estimated potential revenue fromthe captured image with the inserted at least one advertisement image atthe particular location calculating by multiplication of the estimatedpotential revenue by an advertisement image classification modifiermodule 1012 multiplying the estimated potential revenue (e.g., onethousand dollars) by a particular modifier (e.g., 1.15) related to theat least one advertisement image (e.g., the more popular the brand inthe advertisement image, or the more closely it is related to thesubject matter of the captured image, the larger the multiplier).

Referring now to FIG. 15B, operation 1108 may include operation 1514depicting receiving the updated estimated potential revenue from themodified captured image that includes the at least one advertisementimage at the particular location, from a remote location. For example,FIG. 10, e.g., FIG. 10B, shows updated estimated potential revenue fromthe captured image with the inserted at least one advertisement image atthe particular location receiving from a remote location module 1014receiving the updated estimated potential revenue from the modifiedcaptured image (e.g., an image of a famous celebrity eating a fast foodcheeseburger) that includes the at least one advertisement image (e.g.,a fast food restaurant image) at the particular location (e.g.,overlapping the body of the famous celebrity), from a remote location(e.g., a valuation server run by an advertising company).

Referring again to FIG. 15B, operation 1108 may include operation 1516depicting calculating the updated estimated potential revenue from themodified captured image that includes the at least one advertisementimage, at least partly based on term data associated with the entitydepicted in the captured image. For example, FIG. 10, e.g., FIG. 10B,shows updated estimated potential revenue from the captured image withthe inserted at least one advertisement image at the particular locationcalculating at least partly based on term data associated with thecaptured entity module 1016 calculating the updated estimated potentialrevenue from the modified captured image (e.g., a picture of two men ona fishing boat with the advertisement) that includes the at least oneadvertisement image (e.g., an image related to fishing, e.g., anadvertisement for fishing poles and other gear), at least partly basedon term data (e.g., a term of service specifying damages for the use ofone of the fisherman's images) associated with the entity (e.g., one ofthe fishermen) depicted in the captured image (e.g., the picture of twomen on the fishing boat).

All of the above U.S. patents, U.S. patent application publications,U.S. patent applications, foreign patents, foreign patent applicationsand non-patent publications referred to in this specification and/orlisted in any Application Data Sheet, are incorporated herein byreference, to the extent not inconsistent herewith.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software(e.g., a high-level computer program serving as a hardwarespecification), firmware, or virtually any combination thereof, limitedto patentable subject matter under 35 U.S.C. 101. In an embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, limited topatentable subject matter under 35 U.S.C. 101, and that designing thecircuitry and/or writing the code for the software (e.g., a high-levelcomputer program serving as a hardware specification) and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link (e.g., transmitter,receiver, transmission logic, reception logic, etc.), etc.)

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.).

It will be further understood by those within the art that if a specificnumber of an introduced claim recitation is intended, such an intentwill be explicitly recited in the claim, and in the absence of suchrecitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to claims containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations).

Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” is used, in general such a constructionis intended in the sense one having skill in the art would understandthe convention (e.g., “ a system having at least one of A, B, and C”would include but not be limited to systems that have A alone, B alone,C alone, A and B together, A and C together, B and C together, and/or A,B, and C together, etc.). In those instances where a conventionanalogous to “at least one of A, B, or C, etc.” is used, in general sucha construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, or C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that typically a disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms unless context dictates otherwise. For example, the phrase “Aor B” will be typically understood to include the possibilities of “A”or “B” or “A and B.”

With respect to the appended claims, those skilled in the art willappreciate that recited operations therein may generally be performed inany order. Also, although various operational flows are presented in asequence(s), it should be understood that the various operations may beperformed in other orders than those which are illustrated, or may beperformed concurrently. Examples of such alternate orderings may includeoverlapping, interleaved, interrupted, reordered, incremental,preparatory, supplemental, simultaneous, reverse, or other variantorderings, unless context dictates otherwise. Furthermore, terms like“responsive to,” “related to,” or other past-tense adjectives aregenerally not intended to exclude such variants, unless context dictatesotherwise.

This application may make reference to one or more trademarks, e.g., aword, letter, symbol, or device adopted by one manufacturer or merchantand used to identify and/or distinguish his or her product from those ofothers. Trademark names used herein are set forth in such language thatmakes clear their identity, that distinguishes them from commondescriptive nouns, that have fixed and definite meanings, or, in many ifnot all cases, are accompanied by other specific identification usingterms not covered by trademark. In addition, trademark names used hereinhave meanings that are well-known and defined in the literature, or donot refer to products or compounds for which knowledge of one or moretrade secrets is required in order to divine their meaning. Alltrademarks referenced in this application are the property of theirrespective owners, and the appearance of one or more trademarks in thisapplication does not diminish or otherwise adversely affect the validityof the one or more trademarks. All trademarks, registered orunregistered, that appear in this application are assumed to include aproper trademark symbol, e.g., the circle R or bracketed capitalization(e.g., [trademark name]), even when such trademark symbol does notexplicitly appear next to the trademark. To the extent a trademark isused in a descriptive manner to refer to a product or process, thattrademark should be interpreted to represent the corresponding productor process as of the date of the filing of this patent application.

Throughout this application, the terms “in an embodiment,” ‘in oneembodiment,” “in an embodiment,” “in several embodiments,” “in at leastone embodiment,” “in various embodiments,” and the like, may be used.Each of these terms, and all such similar terms should be construed as“in at least one embodiment, and possibly but not necessarily allembodiments,” unless explicitly stated otherwise. Specifically, unlessexplicitly stated otherwise, the intent of phrases like these is toprovide non-exclusive and non-limiting examples of implementations ofthe invention. The mere statement that one, some, or may embodimentsinclude one or more things or have one or more features, does not implythat all embodiments include one or more things or have one or morefeatures, but also does not imply that such embodiments must exist. Itis a mere indicator of an example and should not be interpretedotherwise, unless explicitly stated as such.

Those skilled in the art will appreciate that the foregoing specificexemplary processes and/or devices and/or technologies arerepresentative of more general processes and/or devices and/ortechnologies taught elsewhere herein, such as in the claims filedherewith and/or elsewhere in the present application.

1. A computationally-implemented method, comprising: acquiring a captured image, wherein the captured image depicts at least one entity; performing a comparison between an estimated potential revenue generated from a distribution of the captured image and an estimated potential liability incurred for the distribution of the captured image; modifying the captured image by placing at least one advertisement image at a particular location of the captured image; and calculating an updated estimated potential revenue from the modified captured image that includes the at least one advertisement image at the particular location.
 2. (canceled)
 3. (canceled)
 4. (canceled)
 5. (canceled)
 6. The computationally-implemented method of claim 1, wherein said acquiring a captured image, wherein the captured image depicts at least one entity comprises: acquiring the captured image from an image capture device, wherein the captured image depicts at least one entity.
 7. The computationally-implemented method of claim 6, wherein said acquiring the captured image from an image capture device, wherein the captured image depicts at least one entity comprises: receiving the captured image from a wearable computer that is configured to covertly capture one or more images, wherein the captured image depicts at least one entity.
 8. The computationally-implemented method of claim 1, wherein said acquiring a captured image, wherein the captured image depicts at least one entity comprises: receiving the captured image from a server, wherein the captured image depicts at least one entity.
 9. The computationally-implemented method of claim 8, wherein said receiving the captured image from a server, wherein the captured image depicts at least one entity comprises: receiving the captured image from the server that has performed one or more processes on the image, wherein the captured image depicts at least one entity.
 10. The computationally-implemented method of claim 9, wherein said receiving the captured image from the server that has performed one or more processes on the image, wherein the captured image depicts at least one entity comprises: receiving the captured image from the server that has determined that a potential revenue generated from the captured image is less than a potential liability incurred from a distribution of the captured image, wherein the captured image depicts at least one entity.
 11. The computationally-implemented method of claim 1, wherein said acquiring a captured image, wherein the captured image depicts at least one entity comprises: acquiring the captured image that has previously been encrypted, wherein the captured image depicts at least one entity.
 12. (canceled)
 13. (canceled)
 14. The computationally-implemented method of claim 11, wherein said acquiring the captured image that has previously been encrypted, wherein the captured image depicts at least one entity comprises: acquiring the captured image that has previously been encrypted through use of a unique device code associated with an image capture device configured to capture the captured image.
 15. The computationally-implemented method of claim 1, wherein said acquiring a captured image, wherein the captured image depicts at least one entity comprises: acquiring the captured image that contains a privacy beacon, wherein the captured image depicts at least one entity.
 16. (canceled)
 17. The computationally-implemented method of claim 15, wherein said acquiring the captured image that contains a privacy beacon, wherein the captured image depicts at least one entity comprises: acquiring the captured image that contains a privacy beacon configured to be used to obtain data that regards the entity depicted in the captured image.
 18. The computationally-implemented method of claim 17, wherein said acquiring the captured image that contains a privacy beacon configured to be used to obtain data that regards the entity depicted in the captured image comprises: acquiring the captured image that contains the privacy beacon configured to be used to obtain term data that includes one or more terms of service that govern distribution of the captured image.
 19. (canceled)
 20. The computationally-implemented method of claim 1, wherein said performing a comparison between an estimated potential revenue generated from a distribution of the captured image and an estimated potential liability incurred for the distribution of the captured image comprises: performing the comparison between an estimated potential revenue generated from the distribution of the captured image to a social networking site, and an estimated potential liability incurred for the distribution of the captured image.
 21. The computationally-implemented method of claim 1, wherein said performing a comparison between an estimated potential revenue generated from a distribution of the captured image and an estimated potential liability incurred for the distribution of the captured image comprises: determining whether the estimated potential revenue generated from a direct sale of the captured image is greater than the estimated potential liability incurred for the distribution of the captured image.
 22. (canceled)
 23. The computationally-implemented method of claim 1, wherein said performing a comparison between an estimated potential revenue generated from a distribution of the captured image and an estimated potential liability incurred for the distribution of the captured image comprises: estimating a revenue value that corresponds to an estimated potential advertisement revenue generated from a distribution of the captured image; obtaining an estimated damage value that corresponds to the estimated potential liability incurred for distribution of the captured image; and performing a comparison between the obtained estimated damage value and the estimated revenue value.
 24. The computationally-implemented method of claim 1, wherein said performing a comparison between an estimated potential revenue generated from a distribution of the captured image and an estimated potential liability incurred for the distribution of the captured image comprises: estimating a revenue value that corresponds to the estimated potential advertisement revenue generated from a distribution of the captured image; and performing a comparison between the estimated revenue value and the estimated potential liability incurred for distribution of the captured image.
 25. The computationally-implemented method of claim 24, wherein said estimating a revenue value that corresponds to the estimated potential advertisement revenue generated from a distribution of the captured image comprises: estimating the revenue value that corresponds to the estimated potential advertisement revenue generated from a post of the captured image to a social networking site.
 26. (canceled)
 27. (canceled)
 28. (canceled)
 29. (canceled)
 30. (canceled)
 31. (canceled)
 32. The computationally-implemented method of claim 1, wherein said performing a comparison between an estimated potential revenue generated from a distribution of the captured image and an estimated potential liability incurred for the distribution of the captured image comprises: obtaining an estimated damage value that corresponds to the estimated potential liability incurred for distribution of the captured image; and performing a comparison between the estimated damage value and the estimated potential revenue generated from the distribution of the captured image.
 33. The computationally-implemented method of claim 32, wherein said obtaining an estimated damage value that corresponds to the estimated potential liability incurred for distribution of the captured image comprises: obtaining the estimated damage value that corresponds to the estimated potential liability incurred for distribution of the captured image at least partly based on term data that includes one or more terms of service that govern the distribution of the image.
 34. (canceled)
 35. The computationally-implemented method of claim 32, wherein said obtaining an estimated damage value that corresponds to the estimated potential liability incurred for distribution of the captured image comprises: obtaining the estimated damage value that corresponds to the estimated potential liability incurred for distribution of the captured image at least partly based on privacy metadata related to the captured image.
 36. The computationally-implemented method of claim 35, wherein said obtaining the estimated damage value that corresponds to the estimated potential liability incurred for distribution of the captured image at least partly based on privacy metadata related to the captured image comprises: obtaining the estimated damage value that corresponds to the estimated potential liability incurred for distribution of the captured image at least partly based on privacy metadata that identifies the entity depicted in the captured image.
 37. The computationally-implemented method of claim 32, wherein said obtaining an estimated damage value that corresponds to the estimated potential liability incurred for distribution of the captured image comprises: obtaining a privacy metadata related to the captured image; and generating an estimated damage value that corresponds to the estimated potential liability incurred for distribution of the captured image, at least partly based on the obtained privacy metadata.
 38. The computationally-implemented method of claim 37, wherein said obtaining a privacy metadata related to the captured image comprises: obtaining a privacy metadata related to a privacy beacon associated with the entity depicted in the captured image.
 39. The computationally-implemented method of claim 38, wherein said obtaining a privacy metadata related to a privacy beacon associated with the entity depicted in the captured image comprises: obtaining the privacy metadata related to a privacy beacon associated with the depicted entity and that was detected in the captured image.
 40. (canceled)
 41. The computationally-implemented method of claim 37, wherein said obtaining a privacy metadata related to the captured image comprises: obtaining the privacy metadata that is configured to facilitate retrieval of term data that regards the entity depicted in the captured image.
 42. (canceled)
 43. (canceled)
 44. The computationally-implemented method of claim 41, wherein said obtaining the privacy metadata that is configured to facilitate retrieval of term data that regards the entity depicted in the captured image comprises: obtaining the privacy metadata that is configured to facilitate retrieval of term data that includes one or more terms of service regarding distribution of the captured image that depicts the entity.
 45. The computationally-implemented method of claim 1, wherein said performing a comparison between an estimated potential revenue generated from a distribution of the captured image and an estimated potential liability incurred for the distribution of the captured image comprises: estimating an advertisement revenue value that corresponds to the estimated potential advertisement revenue generated from the distribution of the captured image with an advertisement placed in the captured image; and performing a comparison between the estimated advertisement revenue value and the estimated potential liability incurred for distribution of the captured image.
 46. The computationally-implemented method of claim 45, wherein said estimating an advertisement revenue value that corresponds to the estimated potential advertisement revenue generated from the distribution of the captured image with an advertisement placed in the captured image comprises: estimating the advertisement revenue value that corresponds to the estimated potential advertisement revenue generated from the distribution of the captured image with the advertisement placed in the captured image at a position away from the depicted entity.
 47. The computationally-implemented method of claim 1, wherein said modifying the captured image by placing at least one advertisement image at a particular location of the captured image comprises: modifying the captured image by placing the at least one advertisement image at the particular location of the captured image, wherein the particular location of the at least one advertisement image is at least partially based on the performed comparison.
 48. The computationally-implemented method of claim 47, wherein said modifying the captured image by placing the at least one advertisement image at the particular location of the captured image, wherein the particular location of the at least one advertisement image is at least partially based on the performed comparison comprises: modifying the captured image by placing at least one advertisement image at the particular location of the captured image, wherein the particular location of the at least one advertisement image is at least partially based on a result of the performed comparison.
 49. The computationally-implemented method of claim 47, wherein said modifying the captured image by placing the at least one advertisement image at the particular location of the captured image, wherein the particular location of the at least one advertisement image is at least partially based on the performed comparison comprises: modifying the captured image by placing the at least one advertisement image at the particular location of the captured image, wherein a distance from the particular location to a center of the captured image is proportional to a size of the comparison between the estimated potential revenue and the estimated potential liability.
 50. (canceled)
 51. (canceled)
 52. (canceled)
 53. The computationally-implemented method of claim 1, wherein said modifying the captured image by placing at least one advertisement image at a particular location of the captured image comprises: modifying the captured image by placing at least one advertisement image at a particular location that covers the depicted entity in the captured image, when the estimated potential liability is greater than a particular amount.
 54. (canceled)
 55. The computationally-implemented method of claim 1, wherein said modifying the captured image by placing at least one advertisement image at a particular location of the captured image comprises: modifying the captured image by placing at least one advertisement image at a particular location that overlaps the depicted entity in the captured image.
 56. (canceled)
 57. (canceled)
 58. The computationally-implemented method of claim 1, wherein said modifying the captured image by placing at least one advertisement image at a particular location of the captured image comprises: modifying the captured image by placing a removable advertisement image at the particular location of the captured image.
 59. (canceled)
 60. (canceled)
 61. (canceled)
 62. (canceled)
 63. (canceled)
 64. (canceled)
 65. The computationally-implemented method of claim 1, wherein said calculating an updated estimated potential revenue from the modified captured image that includes the at least one advertisement image at the particular location comprises: estimating an updated potential revenue from the modified captured image that includes the at least one advertisement image at the particular location.
 66. The computationally-implemented method of claim 65, wherein said estimating an updated potential revenue from the modified captured image that includes the at least one advertisement image at the particular location comprises: estimating the updated potential revenue from the modified captured image that includes the at least one advertisement image through estimation of a number of clicks on the at least one advertisement image.
 67. (canceled)
 68. (canceled)
 69. (canceled)
 70. (canceled)
 71. The computationally-implemented method of claim 1, wherein said calculating an updated estimated potential revenue from the modified captured image that includes the at least one advertisement image at the particular location comprises: receiving the updated estimated potential revenue from the modified captured image that includes the at least one advertisement image at the particular location, from a remote location.
 72. The computationally-implemented method of claim 1, wherein said calculating an updated estimated potential revenue from the modified captured image that includes the at least one advertisement image at the particular location comprises: calculating the updated estimated potential revenue from the modified captured image that includes the at least one advertisement image, at least partly based on term data associated with the entity depicted in the captured image.
 73. (canceled)
 74. A computationally-implemented system, comprising: circuitry for acquiring a captured image, wherein the captured image depicts at least one entity; circuitry for performing a comparison between an estimated potential revenue generated from a distribution of the captured image and an estimated potential liability incurred for the distribution of the captured image; circuitry for modifying the captured image by placing at least one advertisement image at a particular location of the captured image; and circuitry for calculating an updated estimated potential revenue from the modified captured image that includes the at least one advertisement image at the particular location. 75-145. (canceled)
 146. A computer program product, comprising: a signal-bearing medium bearing: one or more instructions for acquiring a captured image, wherein the captured image depicts at least one entity; one or more instructions for performing a comparison between an estimated potential revenue generated from a distribution of the captured image and an estimated potential liability incurred for the distribution of the captured image; one or more instructions for modifying the captured image by placing at least one advertisement image at a particular location of the captured image; and one or more instructions for calculating an updated estimated potential revenue from the modified captured image that includes the at least one advertisement image at the particular location.
 147. (canceled) 